A Unified Segmentation Network for Multi-Organ Lesion Detection in Two-Dimensional Grayscale Ultrasound Images
Hanshuo Xing, Yan Jiang, Xinyu Cao, Yin Fang, Peiyan Wu, Wenbo Song, Xinglong Wu · 2023
In recent years, computer-aided diagnostic systems had found wide application in ultrasound image analysis to enhance reliability and alleviate the workload of ultrasound practitioners. The advent of deep learning, particularly the rise of Transformer, has significantly improved the performance of various medical diagnostic tasks, including breast cancer detection, thyroid nodule segmentation, fetal pathology assessment, primary thyroid cancer lymph node metastasis prediction, prostate cancer localization, and brachial plexus nerve system diagnosis. However, most deep learning-based ultrasound lesion segmentation algorithms focus on specific organ types and lack a generalized approach for multi-organ lesion segmentation. This paper presents a unified segmentation network for two-dimensional grayscale ultrasound images, utilizing medical priors to guide the network in learning the relationships between multiple targets. The proposed network introduces a Conv Former module to effectively fuse features at different scales, promoting multi-scale information recognition. Additionally, a self-attention mechanism is introduced to capture internal correlations within the features, thereby reducing reliance on external information. Comprehensive experiments validate the effectiveness and scalability of the proposed framework. Superior performance is achieved compared to the current state-of-the-art segmentation models on a unified ultrasound dataset comprising over 6,000 images and five anatomical sites. This research contributes to the application of computer-aided diagnosis in clinical ultrasound, significantly reducing the workload of healthcare professionals.