SYSTEMS AND METHODS FOR AUTOMATED SPINE SEGMENTATION AND ASSESSMENT OF DEGENERATION USING DEEP LEARNING
A deep learning-based system is provided for spine segmentation and classification. The method comprises: (a) receiving a medical image of a subject, where the medical image captures one or more structures of the subject; (b) applying a first deep network to the medical image and outputting a detection result, where the detection result comprises at least a segmentation map of the one or more structures and a location predicted for the one or more structures; (c) generating an input to a second deep network based at least in part on the location predicted in (b); and (d) predicting a degenerative condition for the one or more structures by processing the input using the second deep network.
1 . A computer-implemented method for spine segmentation and classification, the method comprising:
(a) receiving a medical image of a subject, wherein the medical image captures one or more structures of the subject;
(b) applying a first deep network to the medical image and outputting a detection result, wherein the detection result comprises at least a segmentation map of the one or more structures and a location predicted for the one or more structures;
(c) generating an input to a second deep network based at least in part on the location predicted in (b); and
(d) predicting a degenerative condition for the one or more structures by processing the input using the second deep network.
2 . The computer-implemented method of claim 1 , wherein the medical image includes a magnetic resonance image and the one or more structures comprise one or more spine structures.
3 . The computer-implemented method of claim 1 , wherein the first deep network comprises a segmentation model with a dual regulation module.
4 . The computer-implemented method of claim 3 , wherein the dual regulation module is trained to predict the location of the one or more structures.
5 . The computer-implemented method of claim 3 , wherein the segmentation model is trained to predict the segmentation map of the one or more structures.
6 . The computer-implemented method of claim 1 , wherein the input to the second deep network comprises one or more patches generated from the medical image based at least in part on the location predicted in (b).
7 . The computer-implemented method of claim 1 , wherein the input to the second deep network comprises one or more attention maps generated from the segmentation map.
8 . The computer-implemented method of claim 1 , wherein the input to the second deep network comprises at least a second medical image of a view that is different from the first medical image.
9 . The computer-implemented method of claim 1 , wherein the second deep network comprises a plurality of branches.
10 . The computer-implemented method of claim 9 , wherein the input comprises patches of at least two different sizes and wherein at least two of the plurality of branches are configured to process the patches of at least two different sizes respectively.
11 . The computer-implemented method of claim 9 , wherein the input comprises patches of at least two different views and wherein at least two of the plurality of branches are configured to process the patches of at least two different views respectively.
12 . A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
(a) receiving a medical image of a subject, wherein the medical image captures one or more structures of the subject;
(b) applying a first deep network to the medical image and outputting a detection result, wherein the detection result comprises at least a segmentation map of the one or more structures and a location predicted for the one or more structures;
(c) generating an input to a second deep network based at least in part on the location predicted in (b); and
(d) predicting a degenerative condition for the one or more structures by processing the input using the second deep network.
13 . The non-transitory computer-readable storage medium of claim 12 , wherein the medical image includes a magnetic resonance image and wherein the one or more structures are spine structures.
14 . The non-transitory computer-readable storage medium of claim 12 , wherein the first deep network comprises a segmentation model with a dual regulation module.
15 . The non-transitory computer-readable storage medium of claim 14 , wherein the dual regulation module is trained to predict the location of the one or more structures.
16 . The non-transitory computer-readable storage medium of claim 14 , wherein the segmentation model is trained to predict the segmentation map of the one or more structures.
17 . The non-transitory computer-readable storage medium of claim 12 , wherein the input to the second deep network comprises one or more patches generated from the medical image based at least in part on the location predicted in (b).
18 . The non-transitory computer-readable storage medium of claim 12 , wherein the input to the second deep network comprises one or more attention maps generated from the segmentation map.
19 . The non-transitory computer-readable storage medium of claim 12 , wherein the input to the second deep network comprises at least a second medical image of a view that is different from the first medical image.
20 . The non-transitory computer-readable storage medium of claim 12 , wherein the second deep network comprises a plurality of branches.