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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Segmenting the Uterus in Monocular Laparoscopic Images without Manual Input
verfasst von : Toby Collins, Adrien Bartoli, Nicolas Bourdel, Michel Canis
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015
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Automatically segmenting organs in monocular laparoscopic images is an important and challenging research objective in computer-assisted intervention. For the uterus this is difficult because of high inter-patient variability in tissue appearance and low-contrast boundaries with the surrounding peritoneum. We present a framework to segment the uterus which is completely automatic, requires only a single monocular image, and does not require a 3D model. Our idea is to use a patient-independent uterus detector to roughly localize the organ, which is then used as a supervisor to train a patient-specific organ segmenter. The segmenter uses a physically-motivated organ boundary model designed specifically for illumination in laparoscopy, which is fast to compute and gives strong segmentation constraints. Our segmenter uses a lightweight CRF that is solved quickly and globally with a single graphcut. On a dataset of 220 images our method obtains a mean DICE score of 92.9%.