AUTONOMOUS MULTIDIMENSIONAL SEGMENTATION OF ANATOMICAL STRUCTURES ON THREE-DIMENSIONAL MEDICAL IMAGING

1 . A method for autonomous multidimensional segmentation of anatomical structures from three-dimensional (3D) scan volumes, the method comprising:

(a) receiving the 3D scan volume comprising a set of medical scan images comprising the anatomical structures;

(b) automatically defining succeeding multidimensional regions of input data used for further processing;

(c) autonomously processing, by means of a pre-trained segmentation convolutional neural network (CNN), the defined multidimensional regions to determine weak segmentation results that define a probable 3D shape, location, and size of the anatomical structures;

(d) automatically combining multiple weak segmentation results by determining segmented voxels that overlap on the weak segmentation results, to obtain raw strong segmentation results with improved accuracy of the segmentation;

(e) autonomously filtering the raw strong segmentation results with a predefined set of filters and parameters for enhancing shape, location, size and continuity of the anatomical structures to obtain filtered strong segmentation results; and

(f) autonomously identifying a plurality of classes of the anatomical structures from the filtered strong segmentation results.

2 . The method according to claim 1 , further comprising, after receiving the 3D scan volume:

autonomously processing the 3D scan volume to perform a semantic and/or binary segmentation of the neighboring anatomical structures, in order to obtain autonomous segmentation results defining a 3D representation of the neighboring anatomical structure parts;

combining the autonomous segmentation results for the neighboring structures with the raw 3D scan volume, thereby increasing the input data dimensionality, in order to enhance the segmentation CNN performance by providing additional information; and

performing multidimensional resizing of the defined succeeding multidimensional regions.

3 . The method according to claim 1 , further comprising visualization of the output including the segmented anatomical structures.

4 . The method according to claim 1 , wherein the segmentation CNN is a fully convolutional neural network model with or without layer skip connections.

5 . The method according to claim 4 , wherein the segmentation CNN includes a contracting path and an expanding path.

6 . The method according to claim 5 , wherein the segmentation CNN further comprises, in the contracting path, a number of convolutional layers and a number of pooling layers, where each pooling layer is preceded by at least one convolutional layer.

7 . The method according to claim 5 , wherein the segmentation CNN further comprises, in the expanding path, a number of convolutional layers and a number of upsampling or deconvolutional layers, where each upsampling or deconvolutional layer is preceded by at least one convolutional layer.

8 . The method according to claim 4 , wherein the segmentation CNN output is improved by Select-Attend-Transfer (SAT) gates.

9 . The method according to claim 4 , wherein the segmentation CNN output is improved by Generative Adversarial Networks (GAN).

10 . The method according to claim 1 , wherein the received medical scan images are collected from an intraoperative scanner.

11 . The method according to claim 1 , wherein the received medical scan images are collected from a presurgical stationary scanner.

12 . A computer-implemented system, comprising:

at least one non-transitory processor-readable storage medium that stores at least one processor-executable instruction or data; and

at least one processor communicably coupled to the at least one non-transitory processor-readable storage medium, wherein the at least one processor is configured to perform the steps of the method of claim 1 .