MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts
Authors:
- Franziska KnuthFKFranziska Knuth
- Bao Ngoc HuynhBHBao Ngoc Huynh
- Aurora Rosvoll GroendahlAGAurora Rosvoll Groendahl
- René Mario WinterRWRené Mario Winter
- Anne NegårdANAnne Negård
- Sebastian MeltzerSMSebastian Meltzer
- Anne Hansen ReeARAnne Hansen Ree
- Kjersti FlatmarkKFKjersti Flatmark
- Svein DuelandSDSvein Dueland
- Knut Håkon HoleKHKnut Håkon Hole
- 17 December 2021
- journal article
- research article
- Published by Medical Journals Sweden AB in Acta Oncologica
- Vol. 61 (2) , 255-263
Abstract
Tumor delineation is time- and labor-intensive and prone to inter- and intraobserver variations. Magnetic resonance imaging (MRI) provides good soft tissue contrast, and functional MRI captures tis...Keywords
Funding Information
- South-Eastern Norway Regional Health Authority (2013002, 2015048, 2016050)
- Norwegian Cancer Society (198116-2018)
This publication has 23 references indexed in Scilit:
- V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation2016
- Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineationMedical Image Analysis, 2016
- Oxaliplatin-containing Preoperative Therapy in Locally Advanced Rectal Cancer: Local Response, Toxicity and Long-term OutcomeClinical Oncology, 2016
- U-Net: Convolutional Networks for Biomedical Image Segmentation2015
- MRI volumetry for prediction of tumour response to neoadjuvant chemotherapy followed by chemoradiotherapy in locally advanced rectal cancerBritish Journal of Radiology, 2015
- scikit-image: image processing in PythonPeerJ, 2014
- Principles and Applications of Diffusion-weighted Imaging in Cancer Detection, Staging, and Treatment Follow-upRadioGraphics, 2011
- Matplotlib: A 2D Graphics EnvironmentComputing in Science & Engineering, 2007
- New Guidelines to Evaluate the Response to Treatment in Solid TumorsJNCI: Journal of the National Cancer Institute, 2000
- The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of VarianceJournal of the American Statistical Association, 1937