DESC:CROSS REFERENCE TO RELATED APPLICATION
This application is based on and derives the benefit of Indian Provisional Application 201741036740 as filed on 16th October, 2017, the contents of which are incorporated herein by reference.
TECHNICAL FIELD
[001] Embodiments herein relate to obstetrics and gynecology, and more particularly to methods and systems for providing clinical aid in performing an assessment of female reproductive organs such as ovaries.
BACKGROUND
[002] Ovary is an organ of the female reproductive system that is responsible for the synthesis of ovum. The ovum when fertilized develops into an embryo. The size of a normal ovary varies with age and its size can increase exponentially for approximately 20 years, after which it gradually reduces.
[003] Quantification of the ovarian volume can aid in diagnosis and management of gynecological conditions such as infertility and cancer. Ovarian volume can be a useful indicator of the response to hyper-stimulation in assisted reproduction. There can be a direct correlation between the number of Non-Growing Follicles (NGF) and the ovarian volume. The dosage protocol for initiating hyper-stimulation can be determined by estimating the number of NGF based on ovarian volume. Based on the Rotterdam criteria, ovarian volume is one of the ultrasonographic indicators to classify the ovary as being poly-cystic or normal. Additionally, ovarian volume can also be considered as a useful marker for screening of ovarian cancer.
[004] Ultrasound scans can be used for diagnosis of disorders in women's reproductive system, such as disorders that causes infertility (such as Poly-Cystic Ovary Syndrome (PCOS)), life threatening diseases (such as ovarian cancer), and so on. Such disorders can be detected manually. However, manual identification of these disorders can be time consuming, have low reproducibility, and high false negative rates. In an example, follicles of size 2-4mm may not be identified reliably in ultrasound scans.
[005] FIG. 1 depicts anatomy of female reproductive system. As depicted in FIG. 1, the female reproductive system comprises of two ovaries, two fallopian tubes, the uterus, the serosa, the cervix, and the vagina. The ovaries are ductless reproductive glands wherein female reproductive cells are produced. A women can have a pair of ovaries, held by a membrane beside the uterus on each side of the lower abdomen. The ovary is responsible for producing the female reproductive cells or ova. During ovulation, a follicle can expels an egg under the stimulation of hormones. When an egg matures, it is released and passed into the fallopian tube towards the uterus. If the ovum is fertilized by the male reproductive cell or sperm, conception happens and pregnancy begins.
[006] During ovulation or assisted reproduction, there are considerable changes in the size, volume, and appearance of the ovary, not only between the individuals but also based on the hormonal status.
[007] An ultrasound imaging system can be used to assess ovarian parameters. The ultrasound imaging system irradiates an ultrasound signal, generated by a transducer of a probe, to the ovary and receives an echo signal. The echo signal is reflected from the ovary, thereby obtaining an image of a part inside the ovary. In particular, the ultrasound imaging system can used for medical purposes, such as internal observation of the ovary, diagnosis of damage in inside parts of the ovary, and so on.
[008] The existing methods for quantifying the ovarian parameters can be operator dependent and time consuming. The quantified parameters may not be reproducible. An accurate delineation or segmentation of the ovarian region can aid a clinician in producing more accurate diagnosis.

OBJECTS
[009] The principal object of the embodiments herein is to disclose methods and systems for providing aid to clinicians for assessment of ovarian parameters.
[0010] Another object of the embodiments herein is to provide aid to clinicians for assessment of the quantified ovarian parameters during In-Vitro Fertilization (IVF).
[0011] Another object of the embodiments herein is to quantify the ovarian parameters of a subject such as volume, diameter, morphology, blood flow, and other relevant ovarian parameters of the ovary, during stimulation cycle of the IVF; and track the quantified ovarian parameters throughout the stimulation cycle.
[0012] Another object of the embodiments herein is to track the quantified ovarian parameters during predefined days of the stimulation cycle of the IVF cycle and compare the quantified ovarian parameters of the subject with quantified ovarian parameters of other subjects during the stimulation cycle of the IVF.
[0013] Another object of the embodiments herein is to classify ovarian and non-ovarian structures, estimate ovarian reserve, detect at least one ovarian disorder, classify ovarian pathologies, and obtain an enhanced visualization of the follicles in the ovary; based on quantified ovarian parameters.
[0014] Another object of the embodiments herein is to retrieve quantified ovarian parameters of plurality of subjects and build nomograms based on a correlation between ovarian volume and ovarian reserve.
[0015] Another object of the embodiments herein is to perform automated segmentation of ovarian region in three-dimensional ultrasound image of the ovary.
[0016] Another object of the embodiments herein is to determine hormonal dosage for assisted reproduction based on the rate of change of quantified ovarian parameters.
[0017] Another object of the embodiments herein is to perform staging of ovarian cancer based on the quantified ovarian parameters.
[0018] Another object of the embodiments herein is to retrieve medical history of the subject (segmented ovarian region), for usage as a reference, for registering during current scanning of the ovary, administering follicle growth, tracking follicles for oocyte aspiration, analyzing rate of change of ovarian volume during stimulation cycle, predicting success of In-Vitro Fertilization (IVF), and so on.
SUMMARY
[0019] Accordingly, the embodiments provide methods and systems for assessing an ovary of a subject for quantification of ovarian parameters by detection and localization of the ovary from ultrasound images. The embodiments include extracting a plurality of two dimensional (2D) radial slices by performing rotational slicing of a three dimensional (3D) scanned image of an ovary of a subject. The embodiments include performing an initial segmentation for determining coarse ovarian boundaries in each of the plurality of 2D radial slices. The embodiments include segmenting ovarian boundaries in each of the plurality of 2D radial slices based on the initial segmentation, for obtaining an ovarian region and a non-ovarian region. The embodiments include generating a 3D mesh structure of the ovary based on the ovarian boundaries in each of the plurality of 2D radial slices. The embodiments include quantifying the at least one ovarian parameter from the 3D mesh structure of the ovary for assessing the at least one ovarian parameter.
[0020] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
BRIEF DESCRIPTION OF FIGURES
[0021] This invention is illustrated in the accompanying drawings, through out which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:
[0022] FIG. 1 depicts anatomy of female reproductive system;
[0023] FIG. 2 depicts various units of an apparatus for assessing an ovary, according to embodiments as disclosed herein;
[0024] FIG. 3a depicts an example tracking of variations in ovarian volume over a predefined period, according to embodiments as disclosed herein;
[0025] FIG. 3b depicts tracking of needle path for oocyte aspiration, according to embodiments as disclosed herein;
[0026] FIG. 4a depict examples of diagnosis of ovarian cancer, according to embodiments as disclosed herein;
[0027] FIG. 4b depict examples of diagnosis of Poly-Cystic Ovary Syndrome (PCOS) by quantifying follicles within the ovarian region, according to embodiments as disclosed herein;
[0028] FIG. 5 depicts an example enhanced visualization of the ovary, according to embodiments as disclosed herein;
[0029] FIG. 6 depicts example tracking of an ovary based on ovarian quantifications performed earlier, according to embodiments as disclosed herein;
[0030] FIGS. 7a-7c depict estimation of ovarian reserve from estimated ovarian volume based on nomograms, according to embodiments as disclosed herein;
[0031] FIG. 8 depicts classification of structures in an ultrasound image of an ovary, according to embodiments as disclosed herein;
[0032] FIGS. 9a and 9b depict example detection and removal of false detection of a structure outside the ovarian region, according to embodiments as disclosed herein;
[0033] FIGS. 9c and 9d depict example detection and removal of a leaking follicle based on segmented ovarian region, according to embodiments as disclosed herein; and
[0034] FIG. 10 is a flowchart depicting a method for accessing ovarian parameters from a scanned image of the ovary, according to embodiments as disclosed herein.

DETAILED DESCRIPTION
[0035] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0036] Embodiments herein disclose methods and systems for assessing an ovary of a subject for quantification of ovarian parameters by detection and segmentation of the ovary from a scanned image of an ovary. In an example, the scanned image can be obtained from a Trans-Vaginal Ultrasound (TVUS) scan of the ovary. The embodiments include analyzing the change of different ovarian parameters during In-Vitro Fertilization (IVF). The embodiments include obtaining a scanned image of the ovary of a subject, at predefined days within the IVF cycle. The scanned image can be used for determining the ovarian parameters of the subject such as size, volume, diameter, morphology, blood flow, and other relevant ovarian parameters. Any changes in the ovarian parameters detected using scanned images obtained at predefined days of the IVF cycle can be tracked. The embodiments include monitoring the changes of ovarian parameters in the IVF cycle. The embodiments include retrieving quantified ovarian parameters of plurality of subjects and build nomograms based on the correlation between ovarian volume and ovarian reserve. The embodiments include performing automated segmentation of ovarian region in three-dimensional ultrasound image of the ovary. The embodiments include determining hormonal dosage for assisted reproduction based on the rate of change of quantified ovarian parameters. The embodiments include performing staging of ovarian cancer based on the quantified ovarian parameters.
[0037] The embodiments include classifying structures within and outside the ovarian boundary based on location of the structures and the type of the structures. The structures can be classified as ovarian or non-ovarian based on the location of the structures in the ovarian region or the non-ovarian region. The type of structures can be classified as a follicle or a blood vessel.
[0038] The embodiments include tracking changes in ovarian and follicular volume over a predefined period. The embodiments include determining and tracking the path through which a needle can be guided for retrieving follicles for oocyte aspiration.
[0039] In an example embodiment herein, the ovarian reserve can be estimated based on the ovarian volume. In an example embodiment herein, ovarian disorders can be diagnosed based on blood flow around the ovarian boundaries. In an example embodiment herein, Poly-Cystic Ovary Syndrome (PCOS) can be diagnosed based on follicular volume quantification.
[0040] Referring now to the drawings, and more particularly to FIGS. 2 through 10, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.
[0041] FIG. 2 depicts various units of an apparatus 200 for accessing an ovary, according to embodiments as disclosed herein. As depicted in FIG. 2, the apparatus 200 includes a localization unit 201, a quantification unit 202, a classification unit 203, a tracking unit 204, a diagnosis unit 205, and a display unit 206. The localization unit 201 can detect an ovary from a 3D image, obtained from a TVUS scan of the ovary of a subject. The localization unit 201 can extract a plurality of 2D radial slices from the 3D scanned image of the ovary. In an embodiment herein, the extraction can be performed using a rotational based slicing approach. The radial slices can be separated from each other by a predefined degree, which is based on the number of radial slices to be extracted from the 3D scanned image. In an example, if 30 slices are extracted, then there is a separation of 6 degrees between the radial slices.
[0042] The localization unit 201 can perform segmentation on each of the plurality of 2D radial slices in order to determine initial ovarian boundaries in each of the plurality of radial slices. Learning based methods can be used for performing the segmentation in each of the plurality of radial slices.
[0043] The localization unit 201 can determine coarse ovarian boundaries in each of the plurality of 2D radial slices based on learning methods. In an embodiment, the deep learning can be used to generate initial segmentation.
[0044] The localization unit 201 can refine the coarse ovarian boundaries to determine (fine) ovarian boundaries in each of the plurality of radial slices. In an embodiment, the coarse ovarian boundaries in each of the plurality of 2D radial slices can be refined by an optimization method, in order to determine the (fine) ovarian boundaries. In an example, a level set based active contour deformation can be used. The level set functions can segment each of the plurality of 2D radial slices into an ovarian region (internal to the ovary) and a non-ovarian region (external to the ovary). The fine ovarian boundaries can be used for distinguishing the ovarian region and the non-ovarian region in each of the plurality of 2D radial slices. Henceforth, the fine ovarian boundaries will be referred ovarian boundaries.
[0045] The localization unit 201 can convert the plurality of 2D slices into a 3D mesh structure. In an embodiment, the 3D mesh structure can be generated by performing spherical parameterization based on the segmented ovarian boundaries in each of the 2D radial slices. The spherical parameterization can be performed along a latitude on the surface of the ovary (herein after referred to as a first direction) and longitude along the ovarian boundaries in each of the 2D radial slices (herein after referred to as a second direction). The parameter value along the first direction and the second direction can be based on a normalized distance along the first direction and the second direction.
[0046] The spherical parameterization can be further used for generating a mesh. A mesh can be represented by a plurality of triangles. The plurality of triangles can be used for constructing the 3D mesh structure by connecting adjacent nodes along the first direction and the second direction.
[0047] The quantification unit 202 can quantify the ovarian volume by estimating the surface of the ovary from the 3D mesh structure. The quantification unit 202 can quantify structures, such as follicles, blood vessels, in the ovarian and the non-ovarian region. The quantification unit 202 can estimate the ovarian reserve based on the quantified ovarian volume. The quantification unit 202 can quantify the blood flow in the ovarian and non-ovarian regions. The quantification unit 202 can quantify the morphology of the 3D ovarian surface.
[0048] The classification unit 203 can utilize the results of the segmentation to classify structures in the scanned image based on the segmentation of the ovary. If the whole structure is within the ovarian region, i.e., within the ovarian boundary, then the structure can be classified as ovarian. If a part of the structure or the whole structure is in the non-ovarian region, i.e., outside the ovarian boundary, then the structure can be classified as non-ovarian. The non-ovarian structures can be removed.
[0049] In an example, consider that a follicle is detected in the non-ovarian region. In another example, a leaked follicle can be detected, wherein a part of the follicle can be in the ovarian region and a part of the follicle can be in the non-ovarian region. In both scenarios, the follicle in the non-ovarian region and the non-ovarian region can be removed by the classification unit 203.
[0050] The classification unit 203 can classify structures, within or outside the ovarian region. The structures can be classified based on its type, such as blood vessels, follicles, and so on.
[0051] The classification unit 203 can classify the structures in the scanned image to determine staging of ovarian cancer
[0052] The classification unit 203 can classify the blood flow within or outside the ovarian region. The blood flow can be used to determining staging of ovarian cancer.
[0053] The tracking unit 204 can track variations, if any, in the ovarian volume over a period of time. In an example, the tracking unit 204 can quantify the ovarian volume of a subject each day for a predefined number of days. Ovarian quantifications such as ovarian volume and follicular volume, quantified each day, can be stored in a database for tracking. In an example, the ovarian volume of the subject quantified at a certain day can be retrieved at a later date.
[0054] The tracking unit 204 can track the path of a needle used to retrieve oocytes from specific follicles in a particular location of the ovary. The oocytes can be obtained by choosing follicles in the ovary with a larger volume, which have been localized or segmented by the localization unit 201.
[0055] The diagnosis unit 205 can perform feature extraction of the structures, detected in the ovary, based on quantification of the structures. In an example, the feature extraction can be performed to diagnose disorders such as Poly-Cystic Ovary Syndrome (PCOS). In an example, the diagnosis unit 205 can diagnose ovarian cancer based on blood flow within and outside the ovarian boundaries.
[0056] The display unit 206 can display the initial and refined ovarian boundaries in each of the 2D radial slices, and the 3D mesh structure of the ovary. The display unit 206 can provide an enhanced visualization of the ovary, wherein the ovarian boundary and the follicles within the boundary are segmented and defined. The display unit 206 can display the validity of the diagnosis of the diagnosis unit 205 by displaying the ovarian disorders. The display unit 206 provides visualization of the ovarian and non-ovarian structures, and their type. The display unit 206 provides visualization of detection of false positives and leaked follicles their subsequent removal.
[0057] FIG. 2 shows exemplary units of the apparatus 200, but it is to be understood that other embodiments are not limited thereon. In other embodiments, the apparatus 200may include less or more number of units. Further, the labels or names of the units are used only for illustrative purpose and does not limit the scope of the invention. One or more units can be combined together to perform same or substantially similar function in the apparatus 200.
[0058] FIG. 3a depicts an example tracking of variations in ovarian volume over a predefined period, according to embodiments as disclosed herein. When a subject/patient visits a qualified person such as a technician, gynecologist, and so on, the ovary of the subject can be scanned using the apparatus 200. The technician or the gynecologist can be co-located with the subject or can be remotely located from the subject. Consider that the ovary of the subject has been scanned previously for two days, viz., day 0 and day 1. During each of those days, the ovary/follicles can be segmented and the volume of the ovary and the follicles can be quantified. On day 2, the apparatus 200can perform quantification of the ovary and load the quantifications of the ovarian volume and the follicles on day 0 and day 1 from a database. The apparatus 200can perform longitudinal tracking of changes in the volume of the ovary and the follicles. Ovary and follicle tracking can be performed, as the relative positions of the follicles with respect to ovary do not change. Effective ovarian tracking can improve follicular tracking during In Vitro Fertilization (IVF) cycles.
[0059] FIG. 3b depicts tracking of needle path for oocyte aspiration, according to embodiments as disclosed herein. For oocyte aspiration a follicle with a greater volume can be selected. The embodiments include guiding the needle though a path, such that the needle reaches the follicle with the greater volume.
[0060] Changes in the volume of the ovary and the volume of the follicles, over a period of days, can be tracked by quantifying the volume of the ovary and the volume of the follicles each day. The ovary can be tracked by superimposition of a scan of the ovary, performed on a previous day, with a scan of the ovary performed on the present day. The superimposition involves mapping of the follicles in the scans. Based on the superimposition, the rate of change in the volume of the ovary and the volume of the follicles can be determined. A follicle which a greater positive rate of change can be selected for retrieval.
[0061] FIG. 4a depicts an example diagnosis of ovarian cancer, according to embodiments as disclosed herein. The symptoms of occurrence of ovarian cancer can be detected based on the segmented ovarian boundary and blood flow across the segmented ovarian boundary.
[0062] FIG. 4b depicts an example diagnosis of PCOS by quantifying follicles within the ovarian region, according to embodiments as disclosed herein. PCOS is a situation where, a subject can have infrequent or prolonged menstrual periods. The ovaries may develop numerous small follicles and fail to regularly release eggs. This can be one of the major causes of infertility. PCOS classification is clinically important for early prediction of IVF outcomes and treatment methods. The localization of the ovarian boundary and quantification of the ovarian volume and the follicles within the ovarian volume can be used as a clinical biomarker for PCOS classification. A comparison between a normal ovary and an ovary with PCOS is depicted in FIG. 4b, wherein the ovary with PCOS is having numerous small follicles.
[0063] FIG. 5 depicts an example enhanced visualization of the ovary, according to embodiments as disclosed herein. As depicted in FIG. 5, the ovary and the follicles inside the ovary are segmented and quantified. The surface of the ovary can be estimated and each follicle inside the surface of the ovary can be numbered for tracking. This allows follicle tracking. In an example, the follicles are numbered in the range 2-8. This provides an enhanced visualization of the ovary and the follicles inside the ovary.
[0064] FIG. 6 depicts example tracking of an ovary based on ovarian quantifications performed earlier, according to embodiments as disclosed herein. Consider that a quantification of the ovarian volume was performed on a previous day. The quantification is performed by segmenting the ovary and the follicles inside the ovary, as depicted in 601. The volume of the ovary and the volume of the follicles inside the ovary are determined, as depicted in 603.
[0065] On the present day, the ovarian volume is again quantified by segmenting the ovary and the follicles, as depicted in 602. The follicles detected in the ovary and are labeled 1-6. The volume of the ovary and the volume of the follicles are determined, as depicted in 604. As depicted in 604, it is detected that the volume of the follicles has increased. A structure 605, in the non-ovarian region, is detected on both the previous and the present days.
[0066] The embodiments can retrieve ovarian quantifications, quantified on the previous day, such as the segmented ovarian region, the ovarian volume, and the volume of the follicles, from a database. These ovarian quantifications can be used as a reference for registering the follicles and administering the follicle growth on the present day, as depicted in 606. The registration allows tracking the growth of the follicular shape for oocyte aspiration, and analyzing the rate of change of the ovarian volume during the stimulation cycle. This allows predicting the success of the IVF process.
[0067] A comparison of follicular volume between the present day and the previous day is depicted in 607. The ovarian boundary, ovarian volume and the follicular volume on the present day are depicted in 608.
[0068] FIGS. 7a-7c depict estimation of ovarian reserve from estimated ovarian volume based on nomograms, according to embodiments as disclosed herein. The ovarian reserve can be used for determining the capacity of the ovary to provide egg cells, which are capable of fertilization resulting in a healthy and successful pregnancy. At advanced maternal ages, the number of eggs that can be successfully fertilized for pregnancy can decline.
[0069] As depicted in FIG. 7a, the 3D mesh structure of the ovary can be used for estimating the surface of the ovary and determining the ovarian volume.
[0070] FIG. 7b is an example graph depicting a relation between ovarian volume and reproductive age of subjects of chronological age 35. The ovarian parameters can be quantified at predefined days during the IVF cycle. Based on the quantified ovarian parameters at the predefined days, for a plurality of subjects, the embodiments include generating and displaying nomograms. The nomograms can be used to predict the ovarian reserve based on the ovarian volume.
[0071] FIG. 7c is a nomogram indicating the relation between chronological age of subjects, ovarian reserve and reproductive age.
[0072] The estimation of the ovarian reserve can help in early prediction of successful IVF procedures and help in reducing cost of going through multiple IVF cycles and hormonal impact. The estimation of the ovarian reserve can be used as an indicator for initiating oocyte cryopreservation.
[0073] FIG. 8 depicts classification of structures in an ultrasound image of an ovary, according to embodiments as disclosed herein. As depicted in FIG. 8, different structures (tissues) in the ovarian region and the non-ovarian region can be classified through a pelvic ultrasound scan. In an example, structures viz., the follicles ‘1’, the ovarian boundary ‘2’, and the blood vessels ‘3’ are detected and classified based on their type.
[0074] FIGS. 9a and 9b depict example detection and removal of false detection of a structure outside the ovarian region, according to embodiments as disclosed herein. The embodiments can classify the detected structures as ovarian or non-ovarian based on their location (ovarian or non-ovarian region). A Region of Interest (ROI) can be automatically selected. As depicted in FIG. 9a, a structure ‘A’ (follicle) is detected within the ROI, which is outside the ovarian boundary, i.e., in the non-ovarian region. As depicted in FIG. 9b, the embodiments can remove the structure ‘A’.
[0075] FIGS. 9c and 9d depict example detection and removal of a leaking follicle based on segmented ovarian region, according to embodiments as disclosed herein. As depicted in FIG. 9c, ‘A’ represents the ovarian boundary. The embodiments can detect a leaking follicle ‘B’, and classify ‘B’ as a non-ovarian structure. Subsequently, as depicted in FIG. 9d, the embodiments can remove the leaking follicle ‘B’.
[0076] Automating the quantification of ovarian volume can speed up the clinical workflow, reduce operator bias, and aid in harnessing the full potential of the ultrasound in clinical practice. Embodiments herein can delineate the ovarian boundary from the ultrasound volumes.
[0077] FIG. 10 is a flowchart 1000 depicting a method for accessing ovarian parameters from a scanned image of the ovary, according to embodiments as disclosed herein. At step 1001, the method includes extracting a plurality of radial slices by performing rotational slicing of a scanned image of an ovary of a subject. The scanned image of the ovary can be obtained from a TVUS scan of the ovary of a subject. The extraction can be performed using a rotational based slicing approach. The radial slices can be separated from each other by a predefined degree, which is based on the number of radial slices to be extracted from the scanned image.
[0078] At step 1002, the method includes generating an initial ovarian boundary in each of the radial slices. Learning based technique can be used to generate the initial ovarian boundary. In an embodiment, the deep learned energy map can be generated using U-Net architecture.
[0079] At step 1003, the method includes segmenting ovarian boundaries in each of the plurality of 2D radial slices. Initially coarse ovarian boundaries can be determined. The coarse ovarian boundaries can be refined in order to determine (fine) ovarian boundary in each of the plurality of 2D radial slices. The coarse ovarian boundaries can be refined by minimizing a cost function .In an embodiment, level set based active contour can be used to minimize the cost function to obtain the refined (fine) ovarian boundaries. The level set functions partitions each of the plurality of radial slices into an external region and an internal region. The internal region is the ovarian region and the external region is the non-ovarian region.
[0080] At step 1004, the method includes generating the 3D mesh structure of the ovary based on the segmented ovarian boundaries in each of the plurality of 2D radial slices. The 3D mesh structure can be generated by performing spherical parameterization based on the segmented ovarian boundaries in each of the 2D radial slices. The spherical parameterization can be performed along latitude on the surface of the ovary and along the ovarian boundaries in each of the 2D radial slices. The spherical parameterization can be used for generating a plurality of triangles. The plurality of triangles can be used for constructing the 3D mesh structure by connecting adjacent nodes along the latitude on the surface of the ovary and along the ovarian boundaries in each of the 2D radial slices.
[0081] At step 1005, the method includes quantifying the volume of the ovary from 3D mesh structure of the ovary. The quantification of the ovarian volume can be performed by estimating the surface of the ovary from the 3D mesh structure.
[0082] The various actions in method 1100 may be performed in the order presented, in a different order or simultaneously. Further, in some embodiments, some actions listed in FIG. 10 may be omitted.
[0083] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the network elements. The network elements shown in FIG. 2 include blocks which can be at least one of a hardware device, or a combination of hardware device and software module.
[0084] The embodiments disclosed herein describe methods and systems for assessing an ovary of a subject for quantification of ovarian parameters by detection and segmentation of the ovary from ultrasound images. Therefore, it is understood that the scope of the protection is extended to such a program and in addition to a computer readable means having a message therein, such computer readable storage means contain program code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The method is implemented in a preferred embodiment through or together with a software program written in e.g. Very high speed integrated circuit Hardware Description Language (VHDL) another programming language, or implemented by one or more VHDL or several software modules being executed on at least one hardware device. The hardware device can be any kind of portable device that can be programmed. The device may also include means which could be e.g. hardware means like e.g. an ASIC, or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. The method embodiments described herein could be implemented partly in hardware and partly in software. Alternatively, the invention may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[0085] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
,CLAIMS:STATEMENT OF CLAIMS
I/We claim:
1. A method for assessing at least one ovarian parameter, the method comprising:
extracting, by a localization unit (201), a plurality of two dimensional (2D) radial slices by performing rotational slicing of a three dimensional (3D) scanned image of an ovary of a subject;
performing, by the localization unit (201), an initial segmentation for determining coarse ovarian boundaries in each of the plurality of 2D radial slices;
segmenting, by the localization unit (201), ovarian boundaries in each of the plurality of 2D radial slices based on the initial segmentation to obtain an ovarian region and a non-ovarian region;
generating, by the localization unit (201), a 3D mesh structure of the ovary based on the ovarian boundaries in each of the plurality of 2D radial slices; and
quantifying, by a quantification unit (202), the at least one ovarian parameter from the 3D mesh structure of the ovary.
2. The method as claimed in claim 1, wherein the at least one ovarian parameter is size of the ovary, ovarian volume, morphology of the ovary, and blood flow in the ovary.
3. The method, as claimed in claim 2, wherein the method further comprises estimating, by the quantification unit (202), an ovarian reserve based on the ovarian volume.
4. The method, as claimed in claim 1, wherein the method further comprises generating a nomogram based on comparison of the at least one ovarian parameter of the subject with at least one ovarian parameter of a plurality of subjects.
5. The method, as claimed in claim 1, wherein the method further comprises, diagnosing, by the diagnosis unit (205), ovarian cancer and a stage of the ovarian cancer based on blood flow in the ovarian region and the non-ovarian region.
6. The method, as claimed in claim 1, wherein the method further comprises, diagnosing, by the diagnosis unit (205), Poly-Cystic Ovary Syndrome (PCOS) based on the at least one ovarian parameter.
7. The method as claimed in claim 1, wherein the method further comprises tracking, by the tracking unit (204), a change in the at least one ovarian parameter.
8. The method as claimed in claim 7, wherein the method further comprises determining hormonal dosage for stimulation of the ovary during an In-Vitro Fertilization (IVF) cycle, based on the changes in the at least one ovarian parameter.
9. The method as claimed in claim 1, wherein the method further comprises classifying, by a classification unit (203), a structure based on at least one of location of the structure and type of the structure, wherein the location is one of the ovarian region and the non-ovarian region, wherein the type is one of follicles, cysts, adnexal masses, adnexa and blood vessels.
10. The method, as claimed in claim 1, wherein the 3D mesh structure of the ovary is generated by:
performing a spherical parameterization along latitude on the surface of the ovary and along the ovarian boundary in each of the plurality of 2D radial slices, wherein parameter value along the latitude on the surface of the ovary and along the ovarian boundary is based on normalized distance along the latitude on the surface of the ovary and along the ovarian boundary;
generating a plurality of triangles based on the spherical parameterization, wherein number of discretized nodes along the latitude on the surface of the ovary and along the ovarian boundary is based on number of 2D radial slices and number of sampled points on the ovarian boundaries in each of the plurality of 2D slices; and
constructing the 3D mesh structure through the plurality of triangles by connecting adjacent nodes along the longitudes on the surface of the ovary and latitudes along the ovarian boundary in each of the plurality of 2D radial slices.
11. The method, as claimed in claim 1, wherein the method further comprises:
obtaining, by a tracking unit (204), an ovarian volume and a follicular volume quantified on at least one previous day, wherein the ovarian volume and the follicular volume is stored in a database;
quantifying, by the quantification unit (202), an ovarian volume and a follicular volume on a present day; and
superimposing, by the tracking unit (204), the ovarian volume and the follicular volume quantified on the present day on the ovarian volume and the follicular volume quantified on the at least one previous day.
12. The method, as claimed in claim 11, wherein the method further comprises:
determining, by a tracking unit (204), follicles greater than a predefined volume among the follicles of the ovary, wherein the follicles greater than the predefined volume are determined based on the superimposition; and
determining, by the tracking unit (204), a path to be followed by a needle probe for retrieving the follicles during oocyte aspiration.
13. An apparatus (200) for assessing ovarian parameters, the apparatus (200) configured to:
extract, by a localization unit (201), a plurality of two dimensional (2D) radial slices by performing rotational slicing of a three dimensional (3D) scanned image of an ovary of a subject;
perform, by the localization unit (201), an initial segmentation for determining coarse ovarian boundaries in each of the plurality of 2D radial slices;
segment, by the localization unit (201), ovarian boundaries in each of the plurality of 2D radial slices based on the initial segmentation to obtain an ovarian region and a non-ovarian region;
generate, by the localization unit (201), a 3D mesh structure of the ovary based on the ovarian boundaries in each of the plurality of 2D radial slices; and
quantify, by a quantification unit (202), the at least one ovarian parameter from the 3D mesh structure of the ovary.
14. The apparatus (200) as claimed in claim 13, wherein the at least one ovarian parameter is size of the ovary, ovarian volume, morphology of the ovary, and blood flow in the ovary.
15. The apparatus (200), as claimed in claim 14, wherein the apparatus (200) is further configured to estimate, by the quantification unit (202), an ovarian reserve based on the ovarian volume.
16. The apparatus (200), as claimed in claim 13, wherein the apparatus (200) is further configured to generate a nomogram based on comparison of the at least one ovarian parameter of the subject with at least one ovarian parameter of a plurality of subjects.
17. The apparatus (200), as claimed in claim 13, wherein the apparatus (200) is further configured to, diagnose, by the diagnosis unit (205), ovarian cancer and a stage of the ovarian cancer based on blood flow in the ovarian region and the non-ovarian region.
18. The apparatus (200), as claimed in claim 13, wherein the apparatus (200) is further configured to, diagnose, by the diagnosis unit (205), Poly-Cystic Ovary Syndrome (PCOS) based on the at least one ovarian parameter.
19. The apparatus (200) as claimed in claim 13, wherein the method further comprises tracking, by the tracking unit (204), a change in the at least one ovarian parameter.
20. The apparatus (200) as claimed in claim 19, wherein the apparatus (200) is further configured to determine hormonal dosage for stimulation of the ovary during an In-Vitro Fertilization (IVF) cycle, based on the changes in the at least one ovarian parameter.
21. The apparatus (200) as claimed in claim 1, wherein the apparatus (200) is further configured to classify, by a classification unit (203), a structure based on at least one of location of the structure and type of the structure, wherein the location is one of the ovarian region and the non-ovarian region, wherein the type is one of follicles, cysts, adnexal masses, adnexa and blood vessels.
22. The apparatus (200), as claimed in claim 13, wherein the 3D mesh structure of the ovary is generated by:
performing a spherical parameterization along latitude on the surface of the ovary and along the ovarian boundary in each of the plurality of 2D radial slices, wherein parameter value along the latitude on the surface of the ovary and along the ovarian boundary is based on normalized distance along the latitude on the surface of the ovary and along the ovarian boundary;
generating a plurality of triangles based on the spherical parameterization, wherein number of discretized nodes along the latitude on the surface of the ovary and along the ovarian boundary is based on number of 2D radial slices and number of sampled points on the ovarian boundaries in each of the plurality of 2D slices; and
constructing the 3D mesh structure through the plurality of triangles by connecting adjacent nodes along the longitudes on the surface of the ovary and latitudes along the ovarian boundary in each of the plurality of 2D radial slices.
23. The apparatus (200), as claimed in claim 13, wherein the apparatus (200) is further configured to:
obtain, by a tracking unit (204), an ovarian volume and a follicular volume quantified on at least one previous day, wherein the ovarian volume and the follicular volume is stored in a database;
quantify, by the quantification unit (202), an ovarian volume and a follicular volume on a present day; and
superimpose, by the tracking unit (204), the ovarian volume and the follicular volume quantified on the present day on the ovarian volume and the follicular volume quantified on the at least one previous day.
24. The apparatus (200), as claimed in claim 23, wherein the apparatus (200) is further configured to:
determine, by a tracking unit (204), follicles greater than a predefined volume among the follicles of the ovary, wherein the follicles greater than the predefined volume are determined based on the superimposition; and
determine, by the tracking unit (204), a path to be followed by a needle probe for retrieving the follicles during oocyte aspiration.