ArticleIEEE transactions on medical imaging2022
Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Automated extraction of the plane of minimal hiatal dimensions and mid-sagittal plane from 3D transperineal ultrasound.Medical physics · 2026Article
- SATU-net: a shadow adaptive tracing U-net for gastric cavity segmentation based on the principle of ultrasound imaging.Scientific reports · 2025Article
- Exploring Radiomics Features Based on H&E Images as Potential Biomarkers for Evaluating Muscle Atrophy: A Preliminary Study.Journal of imaging informatics in medicine · 2024Article
- Semi-Supervised Medical Image Segmentation Based on Deep Consistent Collaborative Learning.Journal of imaging · 2024Article
- Masked Modeling-Based Ultrasound Image Classification via Self-Supervised Learning.IEEE open journal of engineering in medicine and biology · 2024Article
- Federated Multi-Organ Segmentation With Inconsistent Labels.IEEE transactions on medical imaging · 2023Article
- Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- Ultrasound Prostate Segmentation Using Adaptive Selection Principal Curve and Smooth Mathematical Model.Journal of digital imaging · 2023Article
- HoloLens augmented reality system for transperineal free-hand prostate procedures.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study.Medical image analysis · 2022Article
- Cross-modal attention for multi-modal image registration.Medical image analysis · 2022Article
- Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Prostate segmentation in transrectal ultrasound (TRUS) image is an essential prerequisite for many prostate-related clinical procedures, which, however, is also a long-standing problem due to the challenges caused by the low image quality and shadow artifacts. In this paper, we propose a Shadow-consistent Semi-supervised Learning (SCO-SSL) method with two novel mechanisms, namely shadow augmentation (Shadow-AUG) and shadow dropout (Shadow-DROP), to tackle this challenging problem. Specifically, Shadow-AUG enriches training samples by adding simulated shadow artifacts to the images to make the network robust to the shadow patterns. Shadow-DROP enforces the segmentation network to infer the prostate boundary using the neighboring shadow-free pixels. Extensive experiments are conducted on two large clinical datasets (a public dataset containing 1,761 TRUS volumes and an in-house dataset containing 662 TRUS volumes). In the fully-supervised setting, a vanilla U-Net equipped with our Shadow-AUG&Shadow-DROP outperforms the state-of-the-arts with statistical significance. In the semi-supervised setting, even with only 20% labeled training data, our SCO-SSL method still achieves highly competitive performance, suggesting great clinical value in relieving the labor of data annotation. Source code is released at https://github.com/DIAL-RPI/SCO-SSL.
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