Yao Sun1,2, Huabei Shi1,2, Shuo Zhang1,2, Pei Wang3, Weiling Zhao4, Xiaobo Zhou4, Kehong Yuan2. 1. Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China. 2. Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China. 3. Sichuan Cancer Hospital and Institute, Chengdu, 610000, China. 4. School of Biomedical Informatics, University of Texas Health Sciences Center at Houston, Houston, TX, 77030, USA.
Abstract
OBJECTIVE: The precise segmentation of organs at risk (OARs) is of importance for improving therapeutic outcomes and reducing injuries of patients undergoing radiotherapy. In this study, we developed a new approach for accurate computed tomography (CT) image segmentation of the eyes and surrounding organs, which is first locating then segmentation (FLTS). METHODS: The FLTS approach was composed of two steps: (a) classification of CT images using convolutional neural networks (CNN), and (b) segmentation of the eyes and surrounding organs using modified U-shape networks. In order to obtain optimal performance, we enhanced our training datasets by random jitter and rotation. RESULTS: This model was trained and verified using the clinical datasets that were delineated by experienced physicians. The dice similarity coefficient (DSC) was employed to evaluate the performance of our segmentation method. The average DSCs for the segmentation of the pituitary, left eye, right eye, left eye lens, right eye lens, left optic nerve, and right optic nerve were 90%, 94%, 93.5%, 84.5%, 84.3%, 80.3%, and 82.2%, respectively. CONCLUSION: We developed a new network-based approach for rapid and accurate CT image segmentation of the eyes and surrounding organs. This method is accurate and efficient, and is suitable for clinical use.
OBJECTIVE: The precise segmentation of organs at risk (OARs) is of importance for improving therapeutic outcomes and reducing injuries of patients undergoing radiotherapy. In this study, we developed a new approach for accurate computed tomography (CT) image segmentation of the eyes and surrounding organs, which is first locating then segmentation (FLTS). METHODS: The FLTS approach was composed of two steps: (a) classification of CT images using convolutional neural networks (CNN), and (b) segmentation of the eyes and surrounding organs using modified U-shape networks. In order to obtain optimal performance, we enhanced our training datasets by random jitter and rotation. RESULTS: This model was trained and verified using the clinical datasets that were delineated by experienced physicians. The dice similarity coefficient (DSC) was employed to evaluate the performance of our segmentation method. The average DSCs for the segmentation of the pituitary, left eye, right eye, left eye lens, right eye lens, left optic nerve, and right optic nerve were 90%, 94%, 93.5%, 84.5%, 84.3%, 80.3%, and 82.2%, respectively. CONCLUSION: We developed a new network-based approach for rapid and accurate CT image segmentation of the eyes and surrounding organs. This method is accurate and efficient, and is suitable for clinical use.
Authors: David Steybe; Philipp Poxleitner; Marc Christian Metzger; Leonard Simon Brandenburg; Rainer Schmelzeisen; Fabian Bamberg; Phuong Hien Tran; Elias Kellner; Marco Reisert; Maximilian Frederik Russe Journal: Int J Comput Assist Radiol Surg Date: 2022-06-03 Impact factor: 3.421