Tumor sensitive matching flow: an approach for ovarian cancer metastasis detection and segmentation

Authors:
Jianfei Liu;Shijun Wang;Marius G. Linguraru;Ronald M. Summers
Affiliations:
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD;Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD;Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington DC;Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD
Venue:
MICCAI'12 Proceedings of the 4th international conference on Abdominal Imaging: computational and clinical applications
Year:
2012

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Abstract

Accurately detecting and segmenting ovarian cancer metastases can have potentially great clinical impact on diagnosis and treatment. The routine machine learning strategies to locate ovarian tumors work poorly because the tumors spread randomly to the entire abdomen. We propose a tumor sensitive matching flow (TSMF) to identify metastasis-caused shape variance between patient organs and atlas. TSMF juxtaposes the role of feature computation/classification, and TSMF vectors highlight tumor regions while dampening all other areas. Therefore, metastases can be accurately located by choosing areas with large TSMF vectors, and segmented by exploiting the level set algorithm on these regions. The proposed algorithm was validated on contrast-enhanced CT data from 11 patients with 26 metastases. 84.6% of metastases were successfully detected, and false positive per patient was 1.2. The volume overlap of the segmented metastases was 63±5.6%, the Dice coefficient was 77±4.2%, and the average surface distance was 3.9±0.95mm.