Contrastive Rendering for Ultrasound Image Segmentation

TL;DR

This paper introduces a novel ultrasound image segmentation framework that formulates boundary estimation as a rendering task combined with contrastive learning, improving boundary accuracy and robustness in challenging US images.

Contribution

The work presents a new boundary estimation approach using rendering and contrastive learning, enhancing fine-grained boundary detection in US images.

Findings

Outperforms state-of-the-art methods on ovarian US dataset

Improves boundary delineation accuracy

Reduces network complexity

Abstract

Ultrasound (US) image segmentation embraced its significant improvement in deep learning era. However, the lack of sharp boundaries in US images still remains an inherent challenge for segmentation. Previous methods often resort to global context, multi-scale cues or auxiliary guidance to estimate the boundaries. It is hard for these methods to approach pixel-level learning for fine-grained boundary generating. In this paper, we propose a novel and effective framework to improve boundary estimation in US images. Our work has three highlights. First, we propose to formulate the boundary estimation as a rendering task, which can recognize ambiguous points (pixels/voxels) and calibrate the boundary prediction via enriched feature representation learning. Second, we introduce point-wise contrastive learning to enhance the similarity of points from the same class and contrastively decrease the similarity of points from different classes. Boundary ambiguities are therefore further addressed. Third, both rendering and contrastive learning tasks contribute to consistent improvement while reducing network parameters. As a proof-of-concept, we performed validation experiments on a challenging dataset of 86 ovarian US volumes. Results show that our proposed method outperforms state-of-the-art methods and has the potential to be used in clinical practice.