ArticlePhysics in medicine and biology2019
Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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
27 citing papers in PubMed, 67 citations in OpenAlex.
- Robust framework for adaptive field-of-view automatic segmentation in vaginal brachytherapy for endometrial cancer.Physics and imaging in radiation oncology · 2026Article
- Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026Review
- Artificial intelligence in the diagnosis and management of gynecologic cancer.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Review
- Automatic catheter digitization in breast brachytherapy.Medical physics · 2025Article
- A review of artificial intelligence in brachytherapy.Journal of applied clinical medical physics · 2025Review
- Fully automatic reconstruction of prostate high-dose-rate brachytherapy interstitial needles using two-phase deep learning-based segmentation and object tracking algorithms.Clinical and translational radiation oncology · 2025Article
- Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients' needs.Clinical and translational radiation oncology · 2024Review
- Article
- The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.Zeitschrift fur medizinische Physik · 2024Review
- Attention-Gated Deep-Learning-Based Automatic Digitization of Interstitial Needles in High-Dose-Rate Brachytherapy for Cervical Cancer.Advances in radiation oncology · 2024Article
- CardioVision: A fully automated deep learning package for medical image segmentation and reconstruction generating digital twins for patients with aortic stenosis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2023Article
- Design and Experimental Validation of a 3D-Printed Embedded-Sensing Continuum Robot for Neurosurgery.Micromachines · 2023Article
- Automated treatment planning framework for brachytherapy of cervical cancer using 3D dose predictions.Physics in medicine and biology · 2023Article
- Article
- Neural network-assisted automated image registration for MRI-guided adaptive brachytherapy in cervical cancer.Zeitschrift fur medizinische Physik · 2022Article
- A Survey on Deep Learning for Precision Oncology.Diagnostics (Basel, Switzerland) · 2022Review
- Deep-learning-assisted algorithm for catheter reconstruction during MR-only gynecological interstitial brachytherapy.Journal of applied clinical medical physics · 2022Article
- Effect of bladder volume on radiation doses to organs at risk and tumor in cervical cancer during image-guided adaptive brachytherapy and treatment outcome analysis.Molecular and clinical oncology · 2021Article
- Tip estimation approach for concentric tube robots using 2D ultrasound images and kinematic model.Medical & biological engineering & computing · 2021Article
- Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm.Sensors (Basel, Switzerland) · 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 5 institutions in 5 countries.
Funding
Abstract
External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality for diagnosing and treating these cancers. However, in contrast to computed tomography (CT) imaging, the appearance of the brachytherapy catheters, through which radiation sources are inserted to reach the cancerous tissue later on, is often variable across images. This paper reports, for the first time, a new deep-learning-based method for fully automatic segmentation of multiple closely spaced brachytherapy catheters in intraoperative MRI. Represented in the data are 50 gynecologic cancer patients treated by MRI-guided HDR brachytherapy. For each patient, a single intraoperative MRI was used. 826 catheters in the images were manually segmented by an expert radiation physicist who is also a trained radiation oncologist. The number of catheters in a patient ranged between 10 and 35. A deep 3D convolutional neural network (CNN) model was developed and trained. In order to make the learning process more robust, the network was trained 5 times, each time using a different combination of shown patients. Finally, each test case was processed by the five networks and the final segmentation was generated by voting on the obtained five candidate segmentations. 4-fold validation was executed and all the patients were segmented. An average distance error of 2.0 ± 3.4 mm was achieved. False positive and false negative catheters were 6.7% and 1.5% respectively. Average Dice score was equal to 0.60 ± 0.17. The algorithm is available for use in the open source software platform 3D Slicer allowing for wide scale testing and research discussion. In conclusion, to the best of our knowledge, fully automatic segmentation of multiple closely spaced catheters from intraoperative MR images was achieved for the first time in gynecological brachytherapy.
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.