ArticleIEEE transactions on medical imaging2020
Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.Radiology. Artificial intelligence · 2024Pooled it
- Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.Journal of medical systems · 2020Pooled it
- Article
- Dissecting self-supervised learning strategies for transfer learning in MRI prostate cancer diagnosis.Scientific reports · 2026Article
- ProSeg: multi-scale context fusion for high-precision prostate segmentation in MRI.Scientific reports · 2026Article
- MuGu:mutual guidance learning between pretrained SAM and lightweight model for medical image segmentation.Scientific reports · 2026Article
- Article
- Deep learning based adaptive and automatic measurement of palpebral margin in eyelid morphology.Scientific reports · 2025Article
- Enhancing Physician-Patient Communication in Oncology Using GPT-4 Through Simplified Radiology Reports: Multicenter Quantitative Study.Journal of medical Internet research · 2025Article
- RaNet: a residual attention network for accurate prostate segmentation in T2-weighted MRI.Frontiers in medicine · 2025Article
- Domain adaptation using AdaBN and AdaIN for high-resolution IVD mesh reconstruction from clinical MRI.International journal of computer assisted radiology and surgery · 2024Article
- Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.Cancers · 2024Review
- An auxiliary diagnostic tool for common fundus diseases based on fundus color photography and light-weight classification models.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency.Neurocomputing · 2023Article
- Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy.IEEE transactions on medical imaging · 2023Article
- Deep Learning-Based Auto-Segmentation of Spinal Cord Internal Structure of Diffusion Tensor Imaging in Cervical Spondylotic Myelopathy.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning on lateral flow immunoassay for the analysis of detection data.Frontiers in computational neuroscience · 2023Article
- CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentation.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2023Article
- Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.IEEE transactions on medical imaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images faces several challenges. The lack of clear edge between the prostate and other anatomical structures makes it challenging to accurately extract the boundaries. The complex background texture and large variation in size, shape and intensity distribution of the prostate itself make segmentation even further complicated. Recently, as deep learning, especially convolutional neural networks (CNNs), emerging as the best performed methods for medical image segmentation, the difficulty in obtaining large number of annotated medical images for training CNNs has become much more pronounced than ever. Since large-scale dataset is one of the critical components for the success of deep learning, lack of sufficient training data makes it difficult to fully train complex CNNs. To tackle the above challenges, in this paper, we propose a boundary-weighted domain adaptive neural network (BOWDA-Net). To make the network more sensitive to the boundaries during segmentation, a boundary-weighted segmentation loss is proposed. Furthermore, an advanced boundary-weighted transfer leaning approach is introduced to address the problem of small medical imaging datasets. We evaluate our proposed model on three different MR prostate datasets. The experimental results demonstrate that the proposed model is more sensitive to object boundaries and outperformed other state-of-the-art methods.
Indexed as
Identifiers
What OpenQuestion holds
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.