DESC:BACKGROUND
[0001] Embodiments of the present specification relate generally to contextual segmentation of
medical images, and more particularly to systems and methods for joint deep learning of
foreground, background, and shape using generative models for use in contextual segmentation of
medical images.
[0002] Segmentation or object delineation from medical images/volumes is a fundamental step
for subsequent quantification tasks that are key enablers of medical diagnosis. In general,
segmentation of images entails detection, coarse segmentation, and segmentation of finer details.
Typically, some challenges in segmentation or object delineation from medical images include
noise inherent in images such as ultrasound images, positron emission tomography (PET) images,
and the like, varying contrast inherent to imaging modalities, multimodal intensity variations of
X-Ray, magnetic resonance (MR), and ultrasound images, and complex shapes within the images.
Traditional techniques generally call for the detection of the object in the images followed by exact
segmentation.
[0003] Moreover, traditional segmentation approaches employ geometric priors,
foreground/background intensity models, and shape priors. Some challenges encountered by the
traditional approaches include initialization of the segmentation task, modeling of complex
textures and/or shapes, hyperparameter tuning, and computational timing. Machine learning
approaches configured to learn complex foreground/background intensities have been used to
circumvent some of these challenges. Also, other approaches include use of shape models that are
developed based on training data. The machine learning approaches and the shape model based
approaches are then plugged into standard segmentation frameworks.
[0004] Recent fully convolutional network (FCN)-based approaches provide a single
framework for end-to-end detection and segmentation of objects enabled via learning contexts and
interactions between shape and texture, for example, U-Net. Moreover, FCN-based approaches
also extend themselves to the generalizability of different problems given appropriate training data.
However, fully convolutional networks (FCNs) require a significant amount of representative
training data to facilitate the learning of the multiple entities such as the foreground, background,
shape, and the contextual interactions of these entities. With limited or insufficient training data,
failures are hard to interpret. Moreover, manual selection of data to improve performance may be
problematic.
BRIEF DESCRIPTION
[0005] In accordance with one aspect of the present specification, a method is disclosed. The
method includes receiving an input image. Furthermore, the method includes obtaining a deep
learning having a triad of predictors. The method also includes processing the input image by a
shape model in the triad of predictors to generate a segmented shape image. Moreover, the method
includes presenting the segmented shape image via a display unit.
[0006] In accordance with another aspect of the present specification, a system is disclosed.
The system includes an image acquisition unit configured to acquire an input image. In addition,
the system includes a deep learning unit including a deep learning model, where the deep learning
model includes a triad of predictors. The deep learning unit is configured to process the input
image by a shape model in the triad of predictors to generate a segmented shape image. Moreover,
the system includes a processor unit communicatively coupled to the deep learning unit and
configured to present the segmented shape image via a display unit.
DRAWINGS
[0007] These and other features and aspects of embodiments of the present specification will
become better understood when the following detailed description is read with reference to the
accompanying drawings in which like characters represent like parts throughout the drawings,
wherein:
[0008] FIG. 1 is a block diagram illustrating a system for image segmentation using a joint
deep learning model, in accordance with aspects of the present specification;
[0009] FIG. 2(a) is an input image supplied to the deep learning model of FIG. 1, in accordance
with aspects of the present specification;
[0010] FIGs. 2(b)-2(d) are tri-channel output images generated subsequent to processing of the
input image of FIG. 2(a) by the deep learning model of FIG. 1, in accordance with aspects of the
present specification;
[0011] FIGs. 3(a)-3(c) are images illustrating a comparison of the performance of the deep
learning model of FIG. 1 with the performance of an extant fully convolutional network in
segmenting a shape boundary from input images, in accordance with aspects of the present
specification;
[0012] FIGs. 4(a)-4(c) are images illustrating a comparison of the performance of the deep
learning model of FIG. 1 with the performance of an extant fully convolutional network in
segmenting a fetal abdominal region from input images, in accordance with aspects of the present
specification;
[0013] FIGs 5(a)-5(c) are images illustrating a comparison of the performance of the deep
learning model of FIG. 1 with the performance of an extant fully convolutional network in
segmenting an abdominal region from input images, in accordance with aspects of the present
specification;
[0014] FIG. 6 is a flow chart of a method for image segmentation using a joint deep learning
model, in accordance with aspects of the present specification; and
[0015] FIG. 7 is a block diagram of a shape regularized deep learning model, in accordance
with aspects of the present specification.
DETAILED DESCRIPTION
[0016] Fully convolutional networks (FCNs) lend themselves well to bringing contexts into
learning for segmentation. In accordance with aspects of the present specification, systems and
methods for contextual segmentation of an image using a hybrid of generative modeling of image
formation using a fully convolutional network (FCN) to jointly learn the triad of foreground (F),
background (B) and shape (S) are presented. Such generative modeling of the triad of the
foreground, background, and shape based on the FCN aids in capturing contexts. Further, the
systems and methods may be used with a smaller training data set. Also, these systems and
methods provide results that are easy to interpret and enable easy transfer of learning across
segmentation problems.
[0017] FIG. 1 is a block diagram illustrating a system 100 for image segmentation using a joint
deep learning model, in accordance with aspects of the present specification. The image
segmentation system 100 is used in contextual segmentation of medical images based on a learning
model generated by a joint deep learning of foreground, background, and shape models. More
particularly, the image segmentation system 100 includes an image acquisition unit 112 configured
to acquire an input image 102 corresponding to a subject.
[0018] In one embodiment, the input image 102 is a two-dimensional (2D) image and the image
segmentation refers to a 2D image segmentation. In another embodiment, the input image 102
may refer to a three-dimensional (3D) image and the image segmentation refers to a 3D image
segmentation. The term ‘subject’ used herein refers to a patient, an organ of interest in the patient,
a machine part, or any other object to be analyzed via the input image 102.
[0019] The image segmentation system 100 further includes a deep learning unit 114 that
includes a deep learning model 104. In one embodiment, the deep learning model 104 is a fully
convolutional network (FCN). Specifically, the deep learning model 104 is implemented as a
multi-channel FCN. In the illustrated embodiment, the deep learning model 104 is a multi-channel
FCN having a triad of predictors 116, 118, 120. The multi-channel FCN may be implemented
based on a parallel U-Net architecture having separate weights for each of the triad of predictors.
In another embodiment, the multi-channel FCN is implemented based on shared U-Net
architecture having shared weights for the triad of predictors.
[0020] In the example of FIG. 1, the deep learning model 104 is depicted as a tri-channel FCN
having a foreground model 116, a background model 118, and a shape model 120. The input
image 102 is provided to the deep learning model 104 and the deep learning model 104 is
configured to generate three output channels 106, 108 and 110. In particular, reference numeral
106 is used to represent a foreground texture image generated by the foreground model 116, while
reference numeral 108 is used to represent a background texture image generated by the
background model 118. Also, a segmented shape image generated by the shape model 120 is
represented by reference numeral 110. The image segmentation system 100 further includes a
processor unit 122 and a memory unit 124 communicatively coupled to the image acquisition unit
112 and the deep learning unit 114 via a communication bus 126.
[0021] In standard FCN formulation, such as the U-Net, given training examples of pairs of
images and segmentations masks ??????, ????????????,??,..??, a framework learns a predictor ?? ??
????. ?? defined by
parameters ?? that minimizes a training loss such as a root-mean-square error (RMSE)
??
??
S ???????????????? ?? ????????
?? . In accordance with aspects of the present specification, a triad of predictors
?? ??
???? ??. ??, ???????? ??. ??, ???????? ??. ?? that minimizes the following possibilities for the training loss may be defined
in accordance with equations (1) and (2).
FBS1: ??
??
S ?????????? ???????? ?? ????. ???????? ?? ???? ??
???? ???????? ?? ??1 ?? ??????. ???????? ?? ???? ??
???? ???????? ?? ????????
??
such that ?? ??
???? ???????? ? ??0,1?? (1)
[0022] The first two terms of equation (1) learn the foreground and background predictors
respectively. The last term of equation (1) learns the representation for the object shape.
[0023] Additionally, a simpler notation may be employed to define the triad of predictors in
accordance with equation (2).
FBS2: ?????? ?? ???? ??
???? ??
?? ?? ??1 ?? ???????????????????????????? ?? ???????? ??
?? ?? ??1 ?? ???????? ??
???????? ?? ???? ??
?? ??????
??
such that ?? ??
? ??0,1?? (2)
[0024] It may be noted that in equation (2), simpler notations have been used. For example, ?? ??
??
is used instead of ?? ??
???? ????????. The second term in equation (2) includes the foreground/background
predictors, while the first term includes an image formation model. The last term in equation (2)
includes a shape predictor.
[0025] In both FBS1 and FBS2 of equations (1) and (2), the predictor ?? ??
?? is influenced by the
predictions of ?? ??
??, ??????. Additionally, the formulations of equations (1) and (2) may be supplemented
with geometric priors such as length shortening, curvature smoothness, a shape dictionary prior,
reflectance, and the like.
[0026] The formulations FBS1 and FBS2 of equations (1) and (2) are implemented as multichannel
regression output FCNs with appropriate loss functions like mean squared error, mean
absolute error, and the like, for texture prediction and binary cross entropy for shape. Specifically,
the output layers of the FCNs include three channels for predicting the foreground texture image
106, the background texture image 108, and the segmented shape image 110, respectively.
[0027] In another embodiment, each of the triad of predictors in equation (1) may be modified
based on a convolutional de-noising autoencoder (CDAE) having a p-dimensional shape projection
(encoder) E and a decoder R. The encoder-decoder pair of the CDAE is configured to provide denoising
of input images based on a convolutional neural network. The encoder E is configured to
project any arbitrary shape S to one of a plurality of ground truth segmentation masks
characterizing a shape space M representative of a geometric prior. The RMSE function is
modified as:
PRE1: ??
?? ??|?????? ?? ???? ? ??????????????|???? ?? ???????????????? ?? ?????? ??
???????? ?? ?????????? ?? ??????????
(3)
where, ?? ??
?? ?? ??????????????.
[0028] The first term in the equation (3) is a projection error cost term and is based on a distance
between the predicted shape and the shape space M. The second term in equation (3) is
representative of a cost term that is based on a distance between the encoded representation of the
segmentation mask and the predicted mask. The third term in the equation (3) is a Euclidean cost
term that is based on a distance between ground truth segmentation masks and the predicted masks
from the shape space M. Although the equation (3) corresponds to a cost function representative
of shape regularization, similar cost functions may be added for background texture regularization
and forward texture regularization in equation (1). It may also be noted that equation (2) may also
be modified in a similar way to account for projection error, representation errors, and Euclidean
errors.
[0029] In one embodiment, the shape regularization of equation (3) may be implemented as
illustrated in FIG. 7. Referring now to FIG. 7, a block diagram of a shape regularized deep learning
model 700, in accordance with one aspect of the present specification, is presented. The shape
regularized deep learning model 700 includes a first fully convolutional network 702 cascaded
with a second fully convolutional network 704.
[0030] Moreover, the first FCN 702 may be referred to as a segmentation network, while the
second FCN 704 may be referred to as a shape regularization network. The first FCN 702 is
configured to process an input image 708 and generate a segmented image 710. The second FCN
704 is configured to constrain the segmented image 710 to an autoencoder output image 712 in a
manifold (represented by M) defined by a plurality of training images 714. In one embodiment, a
vanilla U-Net architecture is used as the first FCN 702 and the second FCN 704 when the subject
is a patient and the input image is a medical image.
[0031] Further, the second FCN 704 includes an encoder (E) and a decoder (R). The output of
the first FCN 702 contributes to the third term in equation (3) and the output of the second FCN
704 contributes to the first two terms of the equation (3). In addition, the second FCN 704 is pretrained
based on a plurality of training images. Also, the first FCN is updated based on a custom
loss function 716. The custom loss function in turn is determined based on the segmented image
710, the autoencoder output image 712, and a ground truth image 714.
[0032] With returning reference to FIG. 1, implementing the image segmentation system 100
as described with respect to FIG. 1 aids in processing the input image 102 to separately
generate/predict the foreground texture image 106, the background texture image 108, and the
segmented shape image 110. Moreover, one or more of the foreground texture image 106, the
background texture image 10, and the segmented shape image 110 may be visualized on a display
unit 128 to aid in providing medical care to the subject such as a patient.
[0033] FIG. 2(a) is an input image 202 supplied to a deep learning model/fully convolutional
network (FCN) of FIG. 1, in accordance with aspects of the present specification. In one
embodiment, the input image may be an ultrasound image 202. Further, FIGs. 2(b)-2(d) are trichannel
output images 204, 206, 208 generated subsequent to processing of the input image 202
of FIG. 2(a) by the FCN. More particularly, FIG. 2(a) is representative of the input image 202
such as an ultrasound image that is provided as an input to the FCN/ deep learning model 104 of
FIG. 1.
[0034] Also, FIG. 2(b) is representative of an output image 204 of a foreground texture
generated by the deep learning model 104. In one example, the foreground texture image 204 is
representative of the foreground texture image 106 of FIG. 1. In a similar fashion, FIG. 2(c) is
representative of an output image 206 of a background texture generated by the deep learning
model 104. In one example, the background texture image 206 is representative of the background
texture image 108 of FIG. 1. Additionally, FIG. 2(d) is representative of an output image 208 of
a segmented shape generated by the deep learning model 104. In one example, the segmented
shape image 208 is representative of the segmented shape image 110 of FIG. 1.
[0035] It may be noted that determining the deep learning model 104 based on the formulations
FBS1 and/or FBS2 of equations (1) and (2) provide a robust shape predictor due to the
complementarity of the triad of predictors. Simultaneously determining the triad of predictors for
a given choice of training data ensures superior deep learning model based image segmentation.
[0036] FIGs. 3(a)-3(c) are images that illustrate a comparison of the performance of the
exemplary deep learning model 104 with the performance of an extant FCN in segmenting a shape
boundary from input images, in accordance with aspects of the present specification. FIG. 3(a) is
representative of an input image 302 such as an ultrasound image that is provided to the deep
learning model 104 and/or an extant FCN such as U-Net.
[0037] Further, FIG. 3(b) is representative of an output image 304 generated by the extant FCN
such as the U-Net. In extant FCN based methods, a larger set training data is needed to abstract
the foreground/background texture, the shape, and relations of the textures with the shape.
Reference numeral 308 is representative of a ground truth of a shape boundary of the object in the
image 304. As seen in the illustrative example of FIG. 3(b), the output image 304 shows
incomplete generation of a shape boundary 310 in regions of poor contrast.
[0038] Moreover, FIG. 3(c) is representative of an output image 308 generated by the deep
learning model 104 of FIG. 1. In FIG. 3(c), reference numeral 312 is representative of a ground
truth of a shape boundary of the object in the image 306. As seen in the illustrative example of
FIG. 3(c), the output image 304 shows a complete shape boundary 314.
[0039] It may be noted that processing the input image 302 via the FBS1 formulation of
equation (1) of the exemplary deep learning model 104 results in the identification of the complete
shape boundary 314, while processing the input image 302 via the U-Net results in the
identification of an incomplete shape boundary 310.
[0040] FIGs. 4(a)-4(c) are images illustrating a comparison of the performance of the
exemplary deep learning model 104 of FIG. 1 with the performance of an extant FCN in
segmenting a fetal abdominal region from input images, in accordance with aspects of the present
specification. FIGs 4(a)-4(c) provide a comparison of performance of currently available
techniques such as U-Net and the exemplary deep learning model 104 in segmenting a fetal
abdominal region from input images such as ultrasound images. FIG. 4(a) is representative of an
input image 402 such as an ultrasound image that is provided to the deep learning model 104 and/or
an extant FCN such as U-Net.
[0041] FIG. 4(b) is representative of an output image 404 generated by processing of the input
ultrasound image 402 by the extant FCN U-Net. In a similar fashion, FIG. 4(c) represents an
output image 406 generated by processing the input ultrasound image 402 by the deep learning
model 104 implemented in accordance with the formulation FBS1 of equation (1).
[0042] In FIG. 4(b), reference numeral 408 is generally representative of a ground truth shape
contour corresponding to a segmented shape of interest, such as the fetal abdominal region in the
image 404. Reference numeral 410 is generally representative of a segmented shape contour
corresponding to the segmented shape of interest in the image 404 that is generated by processing
the input ultrasound image 402 by the U-Net.
[0043] Similarly, in FIG. 4(c), reference numeral 412 is generally representative of a ground
truth shape contour corresponding to the segmented shape of interest, such as the fetal abdominal
region in the image 406. Reference numeral 414 is generally representative of a segmented shape
contour corresponding to the segmented shape of interest in the image 406 that is generated by
processing the input ultrasound image 402 by the deep learning model 104.
[0044] As depicted in the illustrative examples of FIGs. 4(a)-4(c), the image 406 of FIG. 4(c)
shows a 4% improvement in DICE coefficient overlap over the ground truth shape contour 412 in
comparison to that of the U-Net generated image 404, which is significant especially in fetal
biometry. Moreover, in the image 406, the segmented shape contour 414 generated by deep
learning model 104 closely follows the fetal abdomen edges due to the modeling of the image
foreground and background in addition to shape modeling.
[0045] It may be noted that joint learning of the foreground and background textures may
obviate overfitting and generalization of the FCN with respect to medical images. With the
foregoing in mind, FIGs. 5(a)-5(c) are images generated from the deep learning model 104 in
response to an exemplary training phase with a set of kidney images and a testing phase with
images of abdomens with different levels of abdominal fat.
[0046] In FIGs 5(a)-5(c), a comparison of the performance of currently available techniques
such as U-Net and the exemplary deep learning model 104 in segmenting an abdominal region
from input images, in accordance with aspects of the present specification, are presented.
[0047] FIG. 5(a) is representative of an input image 502 such as an ultrasound image of an
abdomen with high fat content that is provided to the deep learning model 104 and/or an extant
FCN such as U-Net. Also, FIG. 5(b) is representative of an output image 504 generated by
processing of the input ultrasound image 502 by an extant FCN U-Net. In a similar fashion, an
output image 506 corresponds to an output generated by processing the input ultrasound image
502 by the deep learning model 104 implemented in accordance with formulation FBS1 of equation
(1).
[0048] In FIG. 5(b), reference numeral 508 is generally representative of a ground truth shape
contour corresponding to a segmented shape of interest, such as the abdominal region in the image
504. Reference numeral 510 is generally representative of a segmented shape contour
corresponding to the segmented shape of interest in the image 504 that is generated by processing
the input ultrasound image 502 by the U-Net.
[0049] In addition, in FIG. 5(c), reference numeral 512 is generally representative of a ground
truth shape contour corresponding to the segmented shape of interest, such as the abdominal region
in the image 506. Reference numeral 514 is generally representative of a segmented shape contour
corresponding to the segmented shape of interest in the image 506 that is generated by processing
the input ultrasound image 502 by the deep learning model 104.
[0050] It may be observed from the image 504 of FIG. 5(b) that the segmented shape contour
510 deviates significantly from ground truth shape contour 508. Furthermore, it may be observed
from the image 506 of FIG. 5(c) that the segmented shape contour 514 and ground truth shape
contour 512 show a significant overlap. Accordingly, it may clearly be seen from FIGs. 5(b) and
5(c) that the segmented shape contour 514 of FIG. 5(c) is more accurate than that of the segmented
shape contour 510 of FIG. 5(b). Consequently, use of the deep learning model 104 results in more
accurate morphological measurements. This can be attributed to the ability of the deep learning
model 104 to learn foreground and background textures leading to robust modeling of context.
[0051] FIG. 6 is a flow chart of a method 600 for segmenting an image using a joint deep
learning model, in accordance with aspects of the present specification.
[0052] The method 600 includes receiving an input image, as indicated by step 602. The input
image corresponds to a subject such as, but not limited to, a patient, an organ of interest, a machine
part, luggage, and the like. Further, at step 604, a deep learning model is obtained. In one
embodiment, the deep learning model includes a triad of predictors configured to predict a
foreground texture, a background texture, and a segmented shape. Moreover, in certain
embodiments, the step of obtaining the deep learning model includes generating a multi-channel
fully convolutional neural network representative of the triad of predictors. In another
embodiment, the step of obtaining the deep learning network includes formulating a joint cost
function based on a plurality of foreground model weights, a plurality of background model
weights, and a plurality of shape model weights. Further, the joint cost function is minimized to
generate the foreground model, the background model, and the shape model. It may be noted that
the foreground model includes the plurality of foreground model weights, the background model
includes the plurality of background model weights, and the shape model includes the plurality of
shape model weights.
[0053] In other embodiments, the joint cost function includes a foreground cost factor, a
background cost factor, and a shape cost factor. The foreground cost factor is representative of a
foreground modelling error, the background cost factor is representative of a background
modelling error, and the shape cost factor is representative of a shape modelling error. The joint
cost function is minimized by simultaneously minimizing the foreground cost factor, the
background cost factor, and the shape cost factor.
[0054] In another embodiment, the joint cost function includes a shape cost factor, an
appearance cost factor, and an overfitting cost factor. Accordingly, in this example, the joint cost
function is minimized by simultaneously minimizing the shape cost factor, the appearance cost
factor, and the overfitting cost factor.
[0055] Also, in one embodiment, the joint cost function is modified based on a priori
information about the foreground, the background, and the shape. Specifically, the a priori
information is representative of a geometric prior such as a length shortening prior, a curvature
smoothness prior, a shape dictionary prior, reflectance, and the like. When the geometric prior is
available, a projection cost factor, a representation cost factor, and/or a Euclidean cost factor are
added to the joint cost function for each of the foreground cost factor, the background cost factor,
and the shape cost factor. In one embodiment, the projection cost factor, the representation cost
factor, and the Euclidean cost factor are generated based on a convolutional denoising autoencoder
(CDAE).
[0056] In addition, at step 606, the input image is processed by a shape model in the triad of
predictors to generate a segmented shape image. Furthermore, the segmented shape image may
be visualized via use of the display unit 128 of FIG. 1, as depicted by step 608. In one embodiment,
when the subject is a patient, the display of the segmented shape image facilitates provision of
medical care to the subject.
[0057] Additionally, the method includes processing the input image by the foreground model
and the background model in the triad of predictors. In particular, the input image is processed by
the foreground model in the triad of predictors to generate a foreground texture image. Similarly,
the input image is processed by the background model in the triad of predictors to generate a
background texture image. Moreover, the foreground image and/or the background image may be
visualized on the display unit 128. In the example where the subject is a patient, the display of the
foreground image and/or the background image facilitates provision of medical care to the subject.
[0058] The system and method for joint deep learning using generative models for contextual
segmentation of medical images presented hereinabove provide an alternative approach to robust
contextual segmentation of medical images via the use of simultaneous learning predictors of
foreground, background, and shape. Moreover, the generative modeling of foreground,
background, and shape advantageously leverages the capabilities of the FCN in capturing context
information. Furthermore, this approach provides results that are easy to interpret despite
constraints of limited training data. Additionally, the approach enables easy transfer of learning
across segmentation problems.
[0059] It is to be understood that not necessarily all such objects or advantages described above
may be achieved in accordance with any particular embodiment. Thus, for example, those skilled
in the art will recognize that the systems and techniques described herein may be embodied or
carried out in a manner that achieves or improves one advantage or group of advantages as taught
herein without necessarily achieving other objects or advantages as may be taught or suggested
herein.
[0060] While the technology has been described in detail in connection with only a limited
number of embodiments, it should be readily understood that the specification is not limited to
such disclosed embodiments. Rather, the technology can be modified to incorporate any number
of variations, alterations, substitutions or equivalent arrangements not heretofore described, but
which are commensurate with the spirit and scope of the claims. Additionally, while various
embodiments of the technology have been described, it is to be understood that aspects of the
specification may include only some of the described embodiments. Accordingly, the
specification is not to be seen as limited by the foregoing description. ,CLAIMS:1. A method (600), comprising:
receiving (602) an input image (102);
obtaining (604) a deep learning model (104) comprising a triad of predictors;
processing (606) the input image (102) by a shape model (120) in the triad of predictors to generate a segmented shape image (110); and
presenting (608) the segmented shape image (110) via a display unit (128).
2. The method (600) as claimed in claim 1, further comprising:
processing the input image (102) by a foreground model (116) in the triad of predictors to generate a foreground texture image (106);
processing the input image (102) by a background model (118) in the triad of predictors to generate a background texture image (108); and
presenting the foreground texture image (106), the background texture image (108), or both the foreground texture image (106) and the background texture image (108) on the display unit (128).
3. The method (600) as claimed in claim 2, wherein obtaining (604) the deep learning model (104) comprises generating a multi-channel fully convolutional neural network representative of the triad of predictors.
4. The method (600) as claimed in claim 3, wherein obtaining (604) the deep learning model (104) comprises:
formulating a joint cost function based on a plurality of foreground model (116) weights, a plurality of background model (118) weights, and a plurality of shape model (120) weights; and
minimizing the joint cost function to generate the foreground model (116) comprising the plurality of foreground model (116) weights, the background model (118) comprising the plurality of background model (118) weights, and the shape model (120) comprising the plurality of shape model (120) weights.
5. The method (600) as claimed in claim 4, wherein minimizing the joint cost function comprises simultaneously minimizing a foreground cost factor, a background cost factor, and a shape cost factor.
6. The method (600) as claimed in claim 4, wherein minimizing the joint cost function comprises simultaneously minimizing a shape cost factor, an appearance cost factor, and an overfitting cost factor.
7. The method (600) as claimed in claim 4, wherein obtaining the deep learning model (104) further comprises modifying the joint cost function based on a geometric prior comprising a length shortening prior, a curvature smoothness prior, a shape dictionary prior, and reflectance.
8. The method (600) as claimed in claim 7, wherein the joint cost function further comprises a projection cost factor, a representation cost factor, and a Euclidean cost factor, and wherein the projection cost factor, the representation cost factor, and the Euclidean cost factor are generated based on a convolutional denoising autoencoder.
9. The method (600) as claimed in claim 3, wherein processing the input image (102) comprises generating at least one of the foreground texture image (106), the background texture image (108), and the segmented shape image (110) using a parallel U-Net architecture comprising separate weights for each of the triad of predictors.
10. The method (600) as claimed in claim 3, wherein processing the input image (102) comprises generating at least one of the foreground texture image (106), the background texture image (108), and the segmented shape image (110) using a shared U-Net architecture comprising shared weights for the triad of predictors.
11. A system (100), comprising:
an image acquisition unit (112) configured to acquire an input image (102);
a deep learning unit (114) comprising a deep learning model (104), wherein the deep learning model (104) comprises a triad of predictors, and wherein the deep learning unit (114) is configured to process the input image (102) by a shape model (120) in the triad of predictors to generate a segmented shape image (110); and
a processor unit (122) communicatively coupled to the deep learning unit (114) and configured to present the segmented shape image (110) via a display unit (128).
12. The system (100) as claimed in claim 11, wherein the deep learning unit (114) is further configured to:
process the input image (102) by a foreground model (116) in the triad of predictors to generate a foreground texture image (106);
process the input image (102) by a background model (118) in the triad of predictors to generate a background texture image (108); and
present the foreground texture image (106), the background texture image (108), or both the foreground texture image (106) and the background texture image (108) on the display unit (128).
13. The system (100) as claimed in claim 12, wherein the deep learning unit (114) is further configured to generate a multi-channel fully convolutional neural network representative of the triad of predictors.
14. The system (100) as claimed in claim 13, wherein the deep learning unit (114) is further configured to:
formulate a joint cost function based on a plurality of foreground model (116) weights, a plurality of background model (118) weights, and a plurality of shape model (120) weights;
minimize the joint cost function to generate the foreground model (116) comprising the plurality of foreground model (116) weights, the background model (118) comprising the plurality of background model (118) weights, and the shape model (120) comprising the plurality of shape model (120) weights.
15. The system (100) as claimed in claim 14, wherein the deep learning unit (114) is further configured to simultaneously minimize a foreground cost factor, a background cost factor, and a shape cost factor.
16. The system (100) as claimed in claim 14, wherein the deep learning unit (114) is configured to simultaneously minimize a shape cost factor, an appearance cost factor, and an overfitting cost factor.
17. The system (100) as claimed in claim 14, wherein the deep learning unit (114) further configured to modify the joint cost function based on a geometric prior comprising a length shortening prior, a curvature smoothness prior, a shape dictionary prior, and reflectance.
18. The system (100) as claimed in claim 14, wherein the joint cost function further comprises a projection cost factor, a representation cost factor, and a Euclidean cost factor, and wherein the projection cost factor, the representation cost factor, and the Euclidean cost factor are generated based on a convolutional denoising autoencoder.
19. The system (100) as claimed in claim 13, wherein the multi-channel fully convolutional neural network is based on a parallel U-Net architecture comprising separate weights for each of the triad of predictors.
20. The system (100) as claimed in claim 13, wherein the multi-channel fully convolutional neural network is based on a shared U-Net architecture comprising shared weights for the triad of predictors.