ArticleMedical image analysis2021
MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Internal feasibility evaluation of SAM-Med3D for cervical cancer clinical target volume segmentation on planning computed tomography.Radiological physics and technology · 2026Article
- Semi-automatic mask guidance enhances 3D tumor segmentation in medical imaging.Communications medicine · 2026Article
- Osegnet-F-Unext: O-Segnet-Fusion-Unext for pulmonary lobe segmentation of Covid-19 using Computed Tomography image.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Development and validation of a multimodal AI-agent system for prognosis analysis of bladder urothelial carcinoma.NPJ precision oncology · 2026Article
- Enhancing Spinal Cord and Canal Segmentation in Degenerative Cervical Myelopathy : The Role of Interactive Learning Models with manual Click.Journal of Korean Neurosurgical Society · 2026Article
- Prior knowledge-guided multimodal deep learning system for biomarker exploration and prognosis prediction of urothelial carcinoma.NPJ digital medicine · 2025Article
- Reservoir-enhanced segment anything model for subsurface diagnosis.Nature communications · 2025Article
- Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.European radiology · 2025Article
- Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods.Tomography (Ann Arbor, Mich.) · 2025Review
- PE-MT: A Perturbation-Enhanced Mean Teacher for Semi-Supervised Image Segmentation.Bioengineering (Basel, Switzerland) · 2025Article
- PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Interactive Cascaded Network for Prostate Cancer Segmentation from Multimodality MRI with Automated Quality Assessment.Bioengineering (Basel, Switzerland) · 2024Article
- Article
- Segment anything in medical images.Nature communications · 2024Article
- Annotation-efficient training of medical image segmentation network based on scribble guidance in difficult areas.International journal of computer assisted radiology and surgery · 2024Article
- SenseCare: a research platform for medical image informatics and interactive 3D visualization.Frontiers in radiology · 2024Article
- Pixel Diffuser: Practical Interactive Medical Image Segmentation without Ground Truth.Bioengineering (Basel, Switzerland) · 2023Article
- HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations.Brain structure & function · 2023Article
- Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions.Journal of clinical medicine · 2023Review
- Safety Helmet Detection Based on YOLOv5 Driven by Super-Resolution Reconstruction.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Networks (CNN) have achieved the state-of-the-art performance for automatic segmentation, they are often limited by the lack of clinically acceptable accuracy and robustness in complex cases. Therefore, interactive segmentation is a practical alternative to these methods. However, traditional interactive segmentation methods require a large number of user interactions, and recently proposed CNN-based interactive segmentation methods are limited by poor performance on previously unseen objects. To solve these problems, we propose a novel deep learning-based interactive segmentation method that not only has high efficiency due to only requiring clicks as user inputs but also generalizes well to a range of previously unseen objects. Specifically, we first encode user-provided interior margin points via our proposed exponentialized geodesic distance that enables a CNN to achieve a good initial segmentation result of both previously seen and unseen objects, then we use a novel information fusion method that combines the initial segmentation with only a few additional user clicks to efficiently obtain a refined segmentation. We validated our proposed framework through extensive experiments on 2D and 3D medical image segmentation tasks with a wide range of previously unseen objects that were not present in the training set. Experimental results showed that our proposed framework 1) achieves accurate results with fewer user interactions and less time compared with state-of-the-art interactive frameworks and 2) generalizes well to previously unseen objects.
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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.