Finding seed points for organ segmentation using example annotations

Ranveer Joyseeree; Henning Müller

doi:10.1117/12.2043037

21 March 2014 Finding seed points for organ segmentation using example annotations

Ranveer Joyseeree, Henning Müller

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 903444 (2014) https://doi.org/10.1117/12.2043037
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Organ segmentation is important in diagnostic medicine to make current decision-support tools more effective and efficient. Performing it automatically can save time and labor. In this paper, a method to perform automatic identification of seed points for the segmentation of organs in three-dimensional (3D) non-annotated, full- body magnetic resonance (MR) and computed tomography (CT) volumes is presented. It uses 3D MR and CT acquisitions along with corresponding organ annotations from the Visual Concept Extraction Challenge in Radiology (VISCERAL) banchmark. A training MR or CT volume is first registered affinely with a carefully-chosen reference volume. The registration transform obtained is then used to warp the annotations accompanying that training volume. The process is repeated for several other training volumes. For each organ of interest, an overlap volume is created by merging the warped training annotations corresponding to it. Next, a 3D probability map for organ location on the reference volume is derived from each overlap volume. The centroid of each probability map is determined and it represents a suitable seed point for segmentation of each organ. Afterwards, the reference volume can be affinely mapped onto any non-annotated volume and the mapping applied to the pre-computed volume containing the centroid and the probability distribution for an organ of interest. Segmentation on the non-annotated volume may then be started using existing region-growing segmentation algorithms with the warped centroid as the seed point and the warped probability distribution as an aid to the stopping criterion. The approach yields very promising results.

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Ranveer Joyseeree and Henning Müller "Finding seed points for organ segmentation using example annotations", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903444 (21 March 2014); https://doi.org/10.1117/12.2043037

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KEYWORDS

Image segmentation

Liver

Lung

3D acquisition

3D image processing

Computed tomography

Detection and tracking algorithms

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