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Nearest neighbor 3D segmentation with context features
Author(s): Evelin Hristova; Heinrich Schulz; Tom Brosch; Mattias P. Heinrich; Hannes Nickisch
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Paper Abstract

Automated and fast multi-label segmentation of medical images is challenging and clinically important. This paper builds upon a supervised machine learning framework that uses training data sets with dense organ annotations and vantage point trees to classify voxels in unseen images based on similarity of binary feature vectors extracted from the data. Without explicit model knowledge, the algorithm is applicable to different modalities and organs, and achieves high accuracy. The method is successfully tested on 70 abdominal CT and 42 pelvic MR images. With respect to ground truth, an average Dice overlap score of 0.76 for the CT segmentation of liver, spleen and kidneys is achieved. The mean score for the MR delineation of bladder, bones, prostate and rectum is 0.65. Additionally, we benchmark several variations of the main components of the method and reduce the computation time by up to 47% without significant loss of accuracy. The segmentation results are – for a nearest neighbor method – surprisingly accurate, robust as well as data and time efficient.

Paper Details

Date Published: 2 March 2018
PDF: 8 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740M (2 March 2018); doi: 10.1117/12.2292181
Show Author Affiliations
Evelin Hristova, Hamburg Univ. of Applied Sciences (Germany)
Philips Research (Germany)
Heinrich Schulz, Philips Research (Germany)
Tom Brosch, Philips Research (Germany)
Mattias P. Heinrich, Univ. zu Lübeck (Germany)
Hannes Nickisch, Philips Research (Germany)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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