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Proceedings Paper

Segmentation of organs at risk in CT volumes of head, thorax, abdomen, and pelvis
Author(s): Miaofei Han; Jinfeng Ma; Yan Li; Meiling Li; Yanli Song; Qiang Li
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Paper Abstract

Accurate segmentation of organs at risk (OARs) is a key step in treatment planning system (TPS) of image guided radiation therapy. We are developing three classes of methods to segment 17 organs at risk throughout the whole body, including brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin. The three classes of segmentation methods include (1) threshold-based methods for organs of large contrast with adjacent structures such as lungs, trachea, and skin; (2) context-driven Generalized Hough Transform-based methods combined with graph cut algorithm for robust localization and segmentation of liver, kidneys and spleen; and (3) atlas and registration-based methods for segmentation of heart and all organs in CT volumes of head and pelvis. The segmentation accuracy for the seventeen organs was subjectively evaluated by two medical experts in three levels of score: 0, poor (unusable in clinical practice); 1, acceptable (minor revision needed); and 2, good (nearly no revision needed). A database was collected from Ruijin Hospital, Huashan Hospital, and Xuhui Central Hospital in Shanghai, China, including 127 head scans, 203 thoracic scans, 154 abdominal scans, and 73 pelvic scans. The percentages of “good” segmentation results were 97.6%, 92.9%, 81.1%, 87.4%, 85.0%, 78.7%, 94.1%, 91.1%, 81.3%, 86.7%, 82.5%, 86.4%, 79.9%, 72.6%, 68.5%, 93.2%, 96.9% for brain, brain stem, eyes, mandible, temporomandibular joints, parotid glands, spinal cord, lungs, trachea, heart, livers, kidneys, spleen, prostate, rectum, femoral heads, and skin, respectively. Various organs at risk can be reliably segmented from CT scans by use of the three classes of segmentation methods.

Paper Details

Date Published: 20 March 2015
PDF: 6 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94133J (20 March 2015); doi: 10.1117/12.2081853
Show Author Affiliations
Miaofei Han, Shanghai United Imaging Healthcare Co., Ltd. (China)
Jinfeng Ma, Shanghai United Imaging Healthcare Co., Ltd. (China)
Yan Li, Shanghai United Imaging Healthcare Co., Ltd. (China)
Meiling Li, Shanghai United Imaging Healthcare Co., Ltd. (China)
Yanli Song, Shanghai United Imaging Healthcare Co., Ltd. (China)
Qiang Li, Shanghai United Imaging Healthcare Co., Ltd. (China)
Shanghai Advanced Research Institute (China)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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