ArticleIEEE access : practical innovations, open solutions2020
Exploring Uncertainty Measures in Bayesian Deep Attentive Neural Networks for Prostate Zonal Segmentation.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis 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
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 80 citations in OpenAlex.
- Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review.Radiology. Artificial intelligence · 2024Pooled it
- A Neural Conditional Random Field Model Using Deep Features and Learnable Functions for End-to-End MRI Prostate Zonal Segmentation.The journal of machine learning for biomedical imaging · 2025Article
- SECP-Net: SE-Connection Pyramid Network for Segmentation of Organs at Risk with Nasopharyngeal Carcinoma.Bioengineering (Basel, Switzerland) · 2023Article
- Evaluation of Spatial Attentive Deep Learning for Automatic Placental Segmentation on Longitudinal MRI.Journal of magnetic resonance imaging : JMRI · 2023Article
- ADCoC: Adaptive Distribution Modeling Based Collaborative Clustering for Disentangling Disease Heterogeneity from Neuroimaging Data.IEEE transactions on emerging topics in computational intelligence · 2023Article
- Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study.Insights into imaging · 2023Article
- Real-Time Evaluation of Perception Uncertainty and Validity Verification of Autonomous Driving.Sensors (Basel, Switzerland) · 2023Article
- CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI.IEEE transactions on medical imaging · 2023Article
- A Hybrid Method for Customer Segmentation in Saudi Arabia Restaurants Using Clustering, Neural Networks and Optimization Learning Techniques.Arabian journal for science and engineering · 2023Article
- Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature.Insights into imaging · 2022Article
- Exploring uncertainty measures in convolutional neural network for semantic segmentation of oral cancer images.Journal of biomedical optics · 2022Article
- Radiomics quality score in renal masses: a systematic assessment on current literature.The British journal of radiology · 2022Review
- Patient-level proteomic network prediction by explainable artificial intelligence.NPJ precision oncology · 2022Article
- Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation.European radiology · 2022Article
- Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2022Article
- Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional network.Journal of biomechanics · 2022Article
- TransMed: Transformers Advance Multi-Modal Medical Image Classification.Diagnostics (Basel, Switzerland) · 2021Article
- The Clinical Value of Explainable Deep Learning for Diagnosing Fungal Keratitis UsingFrontiers in medicine · 2021Article
- Multiple Sclerosis Lesion Segmentation in Brain MRI Using Inception Modules Embedded in a Convolutional Neural Network.Journal of healthcare engineering · 2021Article
- Deep Learning Enables Prostate MRI Segmentation: A Large Cohort Evaluation With Inter-Rater Variability Analysis.Frontiers in oncology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Automatic segmentation of prostatic zones on multiparametric MRI (mpMRI) can improve the diagnostic workflow of prostate cancer. We designed a spatial attentive Bayesian deep learning network for the automatic segmentation of the peripheral zone (PZ) and transition zone (TZ) of the prostate with uncertainty estimation. The proposed method was evaluated by using internal and external independent testing datasets, and overall uncertainties of the proposed model were calculated at different prostate locations (apex, middle, and base). The study cohort included 351 MRI scans, of which 304 scans were retrieved from a de-identified publicly available datasets (PROSTATEX) and 47 scans were extracted from a large U.S. tertiary referral center (external testing dataset; ETD)). All the PZ and TZ contours were drawn by research fellows under the supervision of expert genitourinary radiologists. Within the PROSTATEX dataset, 259 and 45 patients (internal testing dataset; ITD) were used to develop and validate the model. Then, the model was tested independently using the ETD only. The segmentation performance was evaluated using the Dice Similarity Coefficient (DSC). For PZ and TZ segmentation, the proposed method achieved mean DSCs of 0.80±0.05 and 0.89±0.04 on ITD, as well as 0.79±0.06 and 0.87±0.07 on ETD. For both PZ and TZ, there was no significant difference between ITD and ETD for the proposed method. This DL-based method enabled the accuracy of the PZ and TZ segmentation, which outperformed the state-of-art methods (Deeplab V3+, Attention U-Net, R2U-Net, USE-Net and U-Net). We observed that segmentation uncertainty peaked at the junction between PZ, TZ and AFS. Also, the overall uncertainties were highly consistent with the actual model performance between PZ and TZ at three clinically relevant locations of the prostate.
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.