ArticleMedical image analysis2021
Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach.
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 10 papers.
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Who cites it
10 citing papers in PubMed, 32 citations in OpenAlex.
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- Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026Review
- New Approaches in Radiotherapy.Cancers · 2025Review
- A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy.Scientific reports · 2025Article
- G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.International journal of computer assisted radiology and surgery · 2025Article
- Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer.Diagnostics (Basel, Switzerland) · 2024Article
- Effect of bladder filling status on positioning errors in post-hysterectomy cervical cancer radiotherapy.Annals of medicine · 2023Article
- A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging.International journal of radiation oncology, biology, physics · 2022Article
- Artificial intelligence can overcome challenges in brachytherapy treatment planning.Journal of applied clinical medical physics · 2022Article
- Fully automated multiorgan segmentation of female pelvic magnetic resonance images with coarse-to-fine convolutional neural network.Medical physics · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Automatic sigmoid colon segmentation in CT for radiotherapy treatment planning is challenging due to complex organ shape, close distances to other organs, and large variations in size, shape, and filling status. The patient bowel is often not evacuated, and CT contrast enhancement is not used, which further increase problem difficulty. Deep learning (DL) has demonstrated its power in many segmentation problems. However, standard 2-D approaches cannot handle the sigmoid segmentation problem due to incomplete geometry information and 3-D approaches often encounters the challenge of a limited training data size. Motivated by human's behavior that segments the sigmoid slice by slice while considering connectivity between adjacent slices, we proposed an iterative 2.5-D DL approach to solve this problem. We constructed a network that took an axial CT slice, the sigmoid mask in this slice, and an adjacent CT slice to segment as input and output the predicted mask on the adjacent slice. We also considered other organ masks as prior information. We trained the iterative network with 50 patient cases using five-fold cross validation. The trained network was repeatedly applied to generate masks slice by slice. The method achieved average Dice similarity coefficients of 0.82 0.06 and 0.88 0.02 in 10 test cases without and with using prior information.
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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.