ArticleMicromachines2021
Three-Dimensional Semantic Segmentation of Pituitary Adenomas Based on the Deep Learning Framework-nnU-Net: A Clinical Perspective.
Article in Micromachines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics of pituitary adenoma using computer vision: a review.Medical & biological engineering & computing · 2024Pooled it
- Systematic review of pituitary gland and pituitary adenoma automatic segmentation techniques in magnetic resonance imaging.Frontiers in radiology · 2026Review
- Automated Segmentation of the Pituitary and Pineal Glands.Human brain mapping · 2025Article
- Assessing Quantitative Performance and Expert Review of Multiple Deep Learning-Based Frameworks for Computed Tomography-based Abdominal Organ Auto-Segmentation.Intelligent oncology · 2025Article
- A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.Neuroradiology · 2025Article
- Preoperative clinical radiomics model based on deep learning in prognostic assessment of patients with gallbladder carcinoma.BMC cancer · 2025Article
- Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.BMC cancer · 2024Article
- deepPGSegNet: MRI-based pituitary gland segmentation using deep learning.Frontiers in endocrinology · 2024Article
- Fully automated imaging protocol independent system for pituitary adenoma segmentation: a convolutional neural network-based model on sparsely annotated MRI.Neurosurgical review · 2023Article
- Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method.Scientific reports · 2022Article
- Editorial for the Special Issue on Advanced Machine Learning Techniques for Sensing and Imaging Applications.Micromachines · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
This study developed and evaluated nnU-Net models for three-dimensional semantic segmentation of pituitary adenomas (PAs) from contrast-enhanced T1 (T1ce) images, with aims to train a deep learning-based model cost-effectively and apply it to clinical practice.
methodsThis study was conducted in two phases. In phase one, two models were trained with nnUNet using distinct PA datasets. Model 1 was trained with 208 PAs in total, and model 2 was trained with 109 primary nonfunctional pituitary adenomas (NFPA). In phase two, the performances of the two models were investigated according to the Dice similarity coefficient (DSC) in the leave-out test dataset.
resultsBoth models performed well (DSC > 0.8) for PAs with volumes > 1000 mm
conclusionsBoth nnU-Net models showed good segmentation performance for PAs > 1000 mm
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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.