Development and validation of fully automated robust deep learning models for multi-organ segmentation from whole-body CT images
Open Access
- 2 February 2025
- journal article
- Published by Elsevier in Physica Medica
- Vol. 130, 104911
Abstract
No abstract availableKeywords
Funding Information
- EU Framework Programme for Research and Innovation Euratom (945196)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SNSF 320030 _ 231742)
This publication has 34 references indexed in Scilit:
- The role of biomarkers and dosimetry parameters in overall and progression free survival prediction for patients treated with personalized 90Y glass microspheres SIRT: a preliminary machine learning studyEuropean Journal of Nuclear Medicine and Molecular Imaging, 2024
- Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learningMedical Physics, 2024
- Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapyEuropean Journal of Nuclear Medicine and Molecular Imaging, 2024
- Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networksEuropean Radiology, 2023
- Fully automated accurate patient positioning in computed tomography using anterior–posterior localizer images and a deep neural network: a dual-center studyEuropean Radiology, 2023
- AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imagingMedical Physics, 2023
- Pediatric chest‐abdomen‐pelvis and abdomen‐pelvis CT images with expert organ contoursMedical Physics, 2022
- Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methodsMedical Physics, 2020
- Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networksEuropean Radiology, 2019
- Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networksMedical Physics, 2016