Reza Mohammadi1, Iman Shokatian2, Mohammad Salehi2, Hossein Arabi3, Isaac Shiri3, Habib Zaidi4. 1. Department of Medical Physics, Iran University of Medical Sciences, Tehran, Iran. Electronic address: Reza021mohammadi@gmail.com. 2. Department of Medical Physics, Iran University of Medical Sciences, Tehran, Iran. 3. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland. 4. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland; Geneva University Neurocenter, Geneva University, CH-1211 Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
Abstract
BACKGROUND AND PURPOSE: Delineation of organs at risk (OARs), such as the bladder, rectum and sigmoid, plays an important role in the delivery of optimal absorbed dose to the target owing to the steep gradient in high-dose rate brachytherapy (HDR-BT). In this work, we propose a deep convolutional neural network-based approach for fast and reproducible auto-contouring of OARs in HDR-BT. MATERIALS AND METHODS: Images of 113 patients with locally-advanced cervical cancer were utilized in this study. We used ResU-Net deep convolutional neural network architecture, which uses long and short skip connections to improve the feature extraction procedure and the accuracy of segmentation. Seventy-three patients chosen randomly were used for training, 10 patients for validation, and 30 patients for testing. Well established quantitative metrics, such as Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD), were used for evaluation. RESULTS: The DSC values for the test dataset were 95.7± 3.7%, 96.6±1.5% and 92.2 ± 3.3% for the bladder, rectum, and sigmoid, respectively. The HD values (mm) were 4.05±5.17, 1.96±2.19 and 3.15±2.03 for the bladder, rectum, and sigmoid, respectively. The ASSDs were 1.04±0.97, 0.45±0.09 and 0.79±0.25 for the bladder, rectum, and sigmoid, respectively. CONCLUSION: The proposed deep convolutional neural network model achieved a good agreement between the predicted and manually defined contours of OARs, thus improving the reproducibility of contouring in brachytherapy workflow.
BACKGROUND AND PURPOSE: Delineation of organs at risk (OARs), such as the bladder, rectum and sigmoid, plays an important role in the delivery of optimal absorbed dose to the target owing to the steep gradient in high-dose rate brachytherapy (HDR-BT). In this work, we propose a deep convolutional neural network-based approach for fast and reproducible auto-contouring of OARs in HDR-BT. MATERIALS AND METHODS: Images of 113 patients with locally-advanced cervical cancer were utilized in this study. We used ResU-Net deep convolutional neural network architecture, which uses long and short skip connections to improve the feature extraction procedure and the accuracy of segmentation. Seventy-three patients chosen randomly were used for training, 10 patients for validation, and 30 patients for testing. Well established quantitative metrics, such as Dice similarity coefficient (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD), were used for evaluation. RESULTS: The DSC values for the test dataset were 95.7± 3.7%, 96.6±1.5% and 92.2 ± 3.3% for the bladder, rectum, and sigmoid, respectively. The HD values (mm) were 4.05±5.17, 1.96±2.19 and 3.15±2.03 for the bladder, rectum, and sigmoid, respectively. The ASSDs were 1.04±0.97, 0.45±0.09 and 0.79±0.25 for the bladder, rectum, and sigmoid, respectively. CONCLUSION: The proposed deep convolutional neural network model achieved a good agreement between the predicted and manually defined contours of OARs, thus improving the reproducibility of contouring in brachytherapy workflow.
Authors: Gerard M Walls; Valentina Giacometti; Aditya Apte; Maria Thor; Conor McCann; Gerard G Hanna; John O'Connor; Joseph O Deasy; Alan R Hounsell; Karl T Butterworth; Aidan J Cole; Suneil Jain; Conor K McGarry Journal: Phys Imaging Radiat Oncol Date: 2022-07-26