System for Segmentation of Anatomical Structures in Cardiac CTA Using Fully Convolutional Neural Networks

A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to implement a system for segmentation of anatomical structures in cardiac computed tomography angiography (CTA) using fully convolutional neural networks, wherein the computer readable program causes the computing device to:

• (a) collect (i) a set of chest CTA images scanned in the axial view and (ii) a manual segmentation of the images, for each one of multiple anatomical structures;
• (b) pre-process the images such that they share the same field of view (FOV);
• (c) use both the images and their manual segmentation to train a supervised deep learning segmentation network, wherein loss is determined from a multi-dice score that is a summation of dice scores for all the multiple anatomical structures, each dice score being computed as the similarity between the manual segmentation and the output of the network for one of the anatomical structures;
• (d) test a given pre-processed image on the trained network, thereby obtaining segmented output of the given image;
• and
• (e) perform post-processing on the segmented output of the given image.

An apparatus comprising:

• at least one processor;
• and
• a memory coupled to the at least one processor, wherein the memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to implement a system for segmentation of anatomical structures in cardiac computed tomography angiography (CTA) using fully convolutional neural networks, wherein the instructions cause the at least one processor to:
• (a) collect (i) a set of chest CTA images scanned in the axial view and (ii) a manual segmentation of the images, for each one of multiple anatomical structures;
• (b) pre-process the images such that they share the same field of view (FOV);
• (c) use both the images and their manual segmentation to train a supervised deep learning segmentation network, wherein loss is determined from a multi-dice score that is a summation of dice scores for all the multiple anatomical structures, each dice score being computed as the similarity between the manual segmentation and the output of the network for one of the anatomical structures;
• (d) test a given pre-processed image on the trained network, thereby obtaining segmented output of the given image;
• and
• (e) perform post-processing on the segmented output of the given image.