Accurate and rapid CT image segmentation of the eyes and surrounding organs for precise radiotherapy
| Autor: | Shuo Zhang, Weiling Zhao, Huabei Shi, Kehong Yuan, Yao Sun, Pei Wang, Xiaobo Zhou |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Organs at Risk
Time Factors genetic structures Computer science medicine.medical_treatment Computed tomography Eye Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine medicine Image Processing Computer-Assisted Humans Computer vision Segmentation Right optic nerve medicine.diagnostic_test business.industry General Medicine Image segmentation eye diseases Left optic nerve Radiation therapy 030220 oncology & carcinogenesis Artificial intelligence Neural Networks Computer business Tomography X-Ray Computed |
| Zdroj: | Medical physics. 46(5) |
| ISSN: | 2473-4209 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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