Liver Segmentation in Ultrasound Images Using Self-Supervised Learning with Physics-inspired Augmentation and Global-Local Refinement

Abstract

Shear Wave Elastography (SWE) is a non-invasive ultrasound method that evaluates changes in liver stiffness, serving as a useful biomarker for liver fibrosis. The proper placement of a region of interest (ROI) on the liver in the B-mode image is imperative for obtaining accurate and dependable results in SWE. In order to develop an automated system for liver fibrosis measurement utilizing SWE, the initial crucial phase involves the segmentation of the liver capsule. This paper presents a novel approach for liver segmentation in ultrasound images using a contrastive self-supervised learning approach. The proposed method leverages a large dataset of unannotated abdominal ultrasound images to learn the feature representations, which are then fine-tuned on the downstream task of liver segmentation. The algorithm is trained in two stages: in the first stage a SimCLR model is trained to learn the feature representations from non-labeled data, and in the second stage these representations are fine-tuned with a smaller annotated dataset of liver segmentation masks. Finally, this is followed by a refinement step using CascadePSP. The study also investigates the use of physics-inspired augmentations, such as sector angle and penetration to improve the performance of the deep learning model on ultrasound images. The proposed approach of SimCLR+ENet was compared against the state-of-the-art method U-Net. Evaluation of the average Dice similarity showed that SimCLR+ENet outperformed U-Net with a result of 90.58% compared to 89.77%. Similarly, the average Huasdorff distance evaluation demonstrated that SimCLR+ENet achieved superior performance with a value of 21.71 compared to U-Net’s 29.53. This highlights the effectiveness of the proposed approach, with performance improvements of 0.9% and 26.5% for the average Dice coefficient and average Hausdorff distance, respectively. The study provides insights into the use of physics-inspired augmentations in the medical ultrasound imaging field and highlights the potential for self-supervised learning in improving segmentation results.