Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation

TL;DR

This paper introduces a deeply self-supervised neural network that leverages contour features to enhance liver segmentation accuracy in CT images, outperforming existing methods with a 2.13% dice score improvement.

Contribution

A novel deep supervision scheme guiding neural networks with contour features for improved liver segmentation in medical imaging.

Findings

Achieved 2.13% higher dice score than state-of-the-art methods.

Demonstrated effectiveness of contour-guided deep supervision.

Validated on 160 abdominal CT images with cross-validation.

Abstract

Objective: Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography (CT) images. Methods: A fully convolutional network was developed to overcome the volumetric image segmentation problem. To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted as a complement with the global shape. The discriminative contour, shape, and deep features were internally merged for the segmentation results. Results and Conclusion: 160 abdominal CT images were used for training and validation. The quantitative evaluation of the proposed network was performed through an eight-fold cross-validation. The result showed that the method, which uses the contour feature, segmented the liver more accurately than the state-of-the-art with a 2.13% improvement in the dice score. Significance: In this study, a new framework was introduced to guide a neural network and learn complementary contour features. The proposed neural network demonstrates that the guided contour features can significantly improve the performance of the segmentation task.