Deep learning and its application to medical image segmentation

TL;DR

This paper discusses the application of deep learning, particularly 3D fully convolutional networks, to improve automatic semantic segmentation in medical imaging, demonstrating state-of-the-art results on CT datasets.

Contribution

It introduces a 3D fully convolutional network architecture for medical image segmentation and evaluates its performance on clinical CT data.

Findings

Achieved state-of-the-art multi-organ segmentation accuracy

Demonstrated effectiveness of 3D FCNs in medical imaging

Validated on clinical CT dataset

Abstract

One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been proven very challenging due to the large variation of anatomy across different patients. However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision. Due to the data driven approaches of hierarchical feature learning in deep learning frameworks, these advances can be translated to medical images without much difficulty. Several variations of deep convolutional neural networks have been successfully applied to medical images. Especially fully convolutional architectures have been proven efficient for segmentation of 3D medical images. In this article, we describe how to build a 3D fully convolutional network (FCN) that can process 3D images in order to produce automatic semantic segmentations. The model is trained and evaluated on a clinical computed tomography (CT) dataset and shows state-of-the-art performance in multi-organ segmentation.