ArticlePhysics in medicine and biology2020
Deep learning-based medical image segmentation with limited labels.
Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 45 citations in OpenAlex.
- Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.Physics in medicine and biology · 2021Trial
- Automatic detection and measurement system for aortic aneurysms using deep learning-based artificial intelligence.The international journal of cardiovascular imaging · 2026Article
- Article
- Computed tomography-based automated measurement of abdominal aortic aneurysm using semantic segmentation with active learning.Scientific reports · 2024Article
- Detailed Image Data Quality and Cleaning Practices for Artificial Intelligence Tools for Breast Cancer.JCO clinical cancer informatics · 2024Article
- Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy.PloS one · 2024Article
- P-TransUNet: an improved parallel network for medical image segmentation.BMC bioinformatics · 2023Article
- Young oncologists benefit more than experts from deep learning-based organs-at-risk contouring modeling in nasopharyngeal carcinoma radiotherapy: A multi-institution clinical study exploring working experience and institute group style factor.Clinical and translational radiation oncology · 2023Article
- Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET.Diagnostics (Basel, Switzerland) · 2022Review
- KaIDA: a modular tool for assisting image annotation in deep learning.Journal of integrative bioinformatics · 2022Article
- Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
Deep learning (DL)-based auto-segmentation has the potential for accurate organ delineation in radiotherapy applications but requires large amounts of clean labeled data to train a robust model. However, annotating medical images is extremely time-consuming and requires clinical expertise, especially for segmentation that demands voxel-wise labels. On the other hand, medical images without annotations are abundant and highly accessible. To alleviate the influence of the limited number of clean labels, we propose a weakly supervised DL training approach using deformable image registration (DIR)-based annotations, leveraging the abundance of unlabeled data. We generate pseudo-contours by utilizing DIR to propagate atlas contours onto abundant unlabeled images and train a robust DL-based segmentation model. With 10 labeled TCIA dataset and 50 unlabeled CT scans from our institution, our model achieved Dice similarity coefficient of 87.9%, 73.4%, 73.4%, 63.2% and 61.0% on mandible, left & right parotid glands and left & right submandibular glands of TCIA test set and competitive performance on our institutional clinical dataset and a third party (PDDCA) dataset. Experimental results demonstrated the proposed method outperformed traditional multi-atlas DIR methods and fully supervised limited data training and is promising for DL-based medical image segmentation application with limited annotated data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.