A recursive ensemble organ segmentation (REOS) framework: application in brain radiotherapy. (11th January 2019)

• Record Type: Journal Article
• Authors: Chen, Haibin; Lu, Weiguo; Chen, Mingli; Zhou, Linghong; Timmerman, Robert; Tu, Dan; Nedzi, Lucien; Wardak, Zabi; Jiang, Steve; Zhen, Xin; Gu, Xuejun
• Is Part Of: Physics in medicine & biology. Volume 64:Number 2(2019:Jan.)
• Publication Date: 2019-01-11
• Subjects: segmentation -- deep learning -- brain radiotherapy; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aaf83c
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: The aim of this work is to develop a novel recursive ensemble OARs segmentation (REOS) framework for accurate organs-at-risk (OARs) automatic segmentation. The REOS recursively segment individual OARs by ensembling images features extracted from an organ localization module and a contour detection module. Both modules are based on a 3D U-Net architecture. The organ localization module is trained for rough segmentation to localize a region of interest (ROI) that encompasses the to-be-delineated OAR, while the contour detection module is trained to segment the OAR within the identified ROI. In this study, the developed REOS framework is applied for brain radiotherapy on segmenting six OARs including the eyes, the brainstem (BS), the optical nerves and the chiasm. Eighty T1-weighted magnetic resonance images (MRI) from 80 brain cancer patients' cases with OARs' gold standard contours were collected for training and testing REOS. On 20 testing cases, the REOS achieve a high segmentation accuracy with Dice similarity coefficient (DSC) mean and standard deviation of 93.9% ± 1.4%, 94.5% ± 2.0%, 90.6% ± 2.7%, on the left and right eyes and the BS, respectively. On small and segmentation-challenging organs, the left and right optical nerves and the chiasm, the REOS achieves DSC of 78.0% ± 10.5%, 82.2% ± 5.9% and 71.1% ± 9.1%. The satisfactory performances demonstrated the effectiveness of the REOS in OARs segmentation.