Description:[001] The present invention generally relates to the field of medical imaging and healthcare technology. More specifically relates to a system and method for automated midline shift quantification in brain CT images using deep learning.
BACKGROUND OF THE INVENTION
[002] The following description provides the information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] The field of medical imaging and diagnostics has long grappled with the need for accurate and efficient quantification of midline shift in brain CT scans. In the traditional landscape of medical imaging and diagnostics, healthcare professionals, particularly radiologists, employed manual measurement techniques as the primary means for midline shift quantification in brain CT images. These manual procedures demanded meticulous visual identification of anatomical landmarks, the careful delineation of measurement lines, and the recording of distances directly onto the CT images. While this approach was widespread, it was labor-intensive and prone to human errors.
[004] An inherent challenge in these manual methods was their subjectivity. Due to their reliance on visual interpretation and hand-drawn lines, there existed a notable risk of interobserver variability. Different healthcare practitioners might arrive at slightly differing measurements when assessing the same CT scan, leading to inconsistencies and potential diagnostic inaccuracies.
[005] Moreover, the manual quantification of midline shift was often time-consuming. The analysis of multiple CT slices and the manual measurement of midline shifts in each slice proved to be a painstaking process. In clinical scenarios demanding swift diagnosis and timely treatment decisions, these time constraints could pose significant challenges.
[006] The limitations of manual methods were particularly evident in emergency medical situations, such as traumatic brain injuries, where prompt assessment and intervention were paramount. The inefficiencies of manual measurement techniques could lead to delayed quantification of midline shift, potentially impacting patient outcomes.
[007] Furthermore, the lack of standardized measurement methodologies across medical facilities and practitioners resulted in inconsistencies in reporting. This lack of uniformity complicated the comparison, aggregation, and analysis of midline shift data from various sources, impeding research and the advancement of treatment protocols.
[008] In addition to manual measurements, some basic image processing tools were employed to enhance contrast and visibility in brain CT scans. However, these tools were inherently limited in their capacity to fully automate the complex task of midline shift quantification accurately.
[009] The present invention stands as a pioneering solution within this context of manual, subjective, and time-consuming methods. It introduces an innovative approach that harnesses the capabilities of deep learning algorithms to automate midline shift quantification, offering a more objective, efficient, and precise means of addressing the enduring challenges in the realm of medical imaging and diagnosis.
SUMMARY OF THE PRESENT INVENTION
[010] According to an embodiment, the present invention discloses a method for the automated quantification of midline shift in brain CT images. This method begins with preprocessing, involving the conversion of DICOM-formatted images to NIFTI format and resampling to a specified resolution to enhance image quality. Subsequently, a trained deep learning model employs inference to detect and segment both the ideal midline and septum within the images. The process further refines these segments through erosion and identifies the topmost and bottommost points to ensure accuracy. Slice selection identifies the ideal slice based on pixel summation, followed by the measurement of midline shift by assessing distances between septum and ideal midline points. To provide clinically relevant data, the calculated midline shift distance is converted into millimeters using voxel information, facilitating more precise diagnosis and treatment planning in the field of medical imaging.
[011] In another embodiment, the method leverages advanced artificial intelligence techniques, specifically based on Convolutional Neural Networks (CNNs), for the detection of the ideal midline and septum within brain CT images. The utilization of CNNs, a subset of deep learning models, significantly enhances the accuracy and efficiency of this automated midline shift quantification process. These CNN-based techniques have been meticulously trained to recognize and precisely segment the ideal midline and septum structures, ensuring that the midline shift measurements are both reliable and rapid. This incorporation of AI technologies not only reduces the potential for human error but also accelerates the overall assessment process, thereby contributing to more efficient and accurate diagnoses and treatment planning in the realm of brain CT image analysis.
[012] In yet another embodiment, the preprocessing further includes subdural windowing of the CT NIFTI image to enhance contrast.
[013] In a further embodiment, the line refinement step comprises joining the topmost and bottommost points in the segmentations after erosion.
[014] According to another embodiment, the method further comprises detecting four equal points along the septum; calculating horizontal distances from each of the four points to the ideal midline; and determining the maximum distances as the midline shift distance.
[015] In yet another embodiment, the method calculates the side of the midline shift.
[016] In yet another embodiment, the midline shift distance is multiplied by voxel information to produce a distance measurement in mm.
[017] In yet another embodiment, the present invention discloses a system for automated midline shift quantification in brain CT images, comprising: a computing device configured to receive brain CT images in DICOM format and preprocess said images by converting them to NIFTI format, resampling them to a predetermined resolution, and applying subdural windowing for contrast enhancement; a deep learning model based on a convolutional neural network (CNN) trained to automatically detect and segment the ideal midline and septum within said brain CT images; an image processing module for refining the detected midline and septum segments to identify topmost and bottommost points; a slice selection module for determining the ideal slice based on the summation of segmentation pixels in each slice; a calculation module for measuring midline shift by quantifying horizontal distances between points on the septum and the ideal midline within the selected slice; a conversion module for translating midline shift distances into millimeters using voxel information; a visualization component for displaying real-time midline shift measurements and side-of-shift information; and a data storage and retrieval mechanisms for storing the quantified midline shift measurements, side-of-shift data, and visualized midline shift information in a medical record or database.
[018] In yet another embodiment, the method further comprises a Picture Archiving and Communication System (PACS) for storing and retrieving brain CT images, wherein the computing device is integrated with said PACS to facilitate seamless data transfer and processing.
[019] In yet another embodiment, the computing device is further configured to provide midline shift measurement reports, including visualizations, in real time to medical professionals, and wherein said reports are accessible via a user interface for use in diagnosis and treatment planning.
[020] It should be understood that the embodiments described herein are provided as examples and can be further modified or adapted based on specific requirements and embodiments of the invention. The carpooling system and method offer a comprehensive and secure solution for carpooling, addressing concerns related to safety, convenience, and cost-sharing while fostering a sense of community among users.
[021] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF THE INVENTION
[022] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
[023] FIG. 1 illustrates an original image, in accordance with an embodiment of the present invention.
[024] FIG. 2 illustrates a subdural window, in accordance with an embodiment of the present invention.
[025] FIG. 3 illustrates an ideal midline, in accordance with an embodiment of the present invention.
[026] FIG. 4 illustrates a septum, in accordance with an embodiment of the present invention.
[027] FIG. 5 illustrates top-bottom detection, in accordance with an embodiment of the present invention.
[028] FIG. 6 illustrates line reconstruction, in accordance with an embodiment of the present invention.
[029] FIG. 7 illustrates midline shift, in accordance with an embodiment of the present invention.
[030] FIG. 8 illustrates a flowchart, in accordance with an embodiment of the present invention.
[031] FIG. 9 illustrates a flow diagram, in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[032] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[033] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element or group of elements with transitional phrases “consisting of”, “consisting”, “selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.
[034] According to an embodiment, the present invention discloses a method for the automated quantification of midline shift in brain CT images. This method begins with preprocessing, involving the conversion of DICOM-formatted images to NIFTI format and resampling to a specified resolution to enhance image quality. Subsequently, a trained deep learning model employs inference to detect and segment both the ideal midline and septum within the images. The process further refines these segments through erosion and identifies the topmost and bottommost points to ensure accuracy. Slice selection identifies the ideal slice based on pixel summation, followed by the measurement of midline shift by assessing distances between septum and ideal midline points. To provide clinically relevant data, the calculated midline shift distance is converted into millimeters using voxel information, facilitating more precise diagnosis and treatment planning in the field of medical imaging.
[035] In another embodiment, the method leverages advanced artificial intelligence techniques, specifically based on Convolutional Neural Networks (CNNs), for the detection of the ideal midline and septum within brain CT images. The utilization of CNNs, a subset of deep learning models, significantly enhances the accuracy and efficiency of this automated midline shift quantification process. These CNN-based techniques have been meticulously trained to recognize and precisely segment the ideal midline and septum structures, ensuring that the midline shift measurements are both reliable and rapid. This incorporation of AI technologies not only reduces the potential for human error but also accelerates the overall assessment process, thereby contributing to more efficient and accurate diagnoses and treatment planning in the realm of brain CT image analysis.
[036] In yet another embodiment, the preprocessing further includes subdural windowing of the CT NIFTI image to enhance contrast.
[037] In a further embodiment, the line refinement step comprises joining the topmost and bottommost points in the segmentations after erosion.
[038] According to another embodiment, the method further comprises detecting four equal points along the septum; calculating horizontal distances from each of the four points to the ideal midline; and determining the maximum distances as the midline shift distance.
[039] In yet another embodiment, the method calculates the side of the midline shift.
[040] In yet another embodiment, the midline shift distance is multiplied by voxel information to produce a distance measurement in mm.
[041] In yet another embodiment, the present invention discloses a system for automated midline shift quantification in brain CT images, comprising: a computing device configured to receive brain CT images in DICOM format and preprocess said images by converting them to NIFTI format, resampling them to a predetermined resolution, and applying subdural windowing for contrast enhancement; a deep learning model based on a convolutional neural network (CNN) trained to automatically detect and segment the ideal midline and septum within said brain CT images; an image processing module for refining the detected midline and septum segments to identify topmost and bottommost points; a slice selection module for determining the ideal slice based on the summation of segmentation pixels in each slice; a calculation module for measuring midline shift by quantifying horizontal distances between points on the septum and the ideal midline within the selected slice; a conversion module for translating midline shift distances into millimeters using voxel information; a visualization component for displaying real-time midline shift measurements and side-of-shift information; and a data storage and retrieval mechanisms for storing the quantified midline shift measurements, side-of-shift data, and visualized midline shift information in a medical record or database.
[042] In yet another embodiment, the method further comprises a Picture Archiving and Communication System (PACS) for storing and retrieving brain CT images, wherein the computing device is integrated with said PACS to facilitate seamless data transfer and processing.
[043] In yet another embodiment, the computing device is further configured to provide midline shift measurement reports, including visualizations, in real time to medical professionals, and wherein said reports are accessible via a user interface for use in diagnosis and treatment planning.
[044] It should be understood that the above detailed description is provided as an example, and the carpooling system and method can be further modified or adapted based on specific requirements and embodiments of the invention. The described features collectively contribute to a comprehensive carpooling solution, ensuring security, convenience, and cost-sharing benefits for users while promoting a greener and more sustainable mode of transportation.
[045] While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention.
, Claims:1. A method for automated midline shift quantification in brain CT images, comprising the steps of:
preprocessing a brain CT image, including converting said image from DICOM format to NIFTI format and resampling to a predetermined resolution;
inference using a trained deep learning model to detect and segment the ideal midline and septum;
line refinement through erosion and detection of topmost and bottommost points in the segmentations;
slice selection to determine the ideal slice;
calculating midline shift by measuring distances between points on the septum and the ideal midline; and
converting the midline shift distance to millimeters using voxel information.
2. The method as claimed in Claim 1, wherein said ideal midline and septum are detected using artificial intelligence techniques based on a convolutional neural network (CNN).
3. The method as claimed in Claim 1, wherein the preprocessing further includes subdural windowing of the CT NIFTI image to enhance contrast.
4. The method as claimed in Claim 1, wherein the line refinement step comprises joining the topmost and bottommost points in the segmentations after erosion.
5. The method as claimed in Claim 1, further comprising:
detecting four equal points along the septum;
calculating horizontal distances from each of the four points to the ideal midline; and
determining the maximum distance among the calculated distances as the midline shift distance.
6. The method as claimed in Claim 1, wherein the method calculates the side of the midline shift.
7. The method as claimed in claim 1, wherein the midline shift distance is multiplied by voxel information to produce a distance measurement in mm.
8. A system for automated midline shift quantification in brain CT images, comprising:
a computing device configured to receive brain CT images in DICOM format and preprocess said images by converting them to NIFTI format, resampling them to a predetermined resolution, and applying subdural windowing for contrast enhancement;
a deep learning model based on a convolutional neural network (CNN) trained to automatically detect and segment the ideal midline and septum within said brain CT images;
an image processing module for refining the detected midline and septum segments to identify topmost and bottommost points;
a slice selection module for determining the ideal slice based on the summation of segmentation pixels in each slice;
a calculation module for measuring midline shift by quantifying horizontal distances between points on the septum and the ideal midline within the selected slice;
a conversion module for translating midline shift distances into millimeters using voxel information;
a visualization component for displaying real-time midline shift measurements and side-of-shift information; and
a data storage and retrieval mechanisms for storing the quantified midline shift measurements, side-of-shift data, and visualized midline shift information in a medical record or database.
9. The system as claimed in claim 8, further comprising a Picture Archiving and Communication System (PACS) for storing and retrieving brain CT images, wherein the computing device is integrated with said PACS to facilitate seamless data transfer and processing.
10. The system as claimed in claim 8, wherein the computing device is further configured to provide midline shift measurement reports, including visualizations, in real time to medical professionals, and wherein said reports are accessible via a user interface for use in diagnosis and treatment planning.