Locating seed points for automatic multi-organ segmentation using non-rigid registration and organ annotations

Ranveer R. Joyseeree; Henning Müller

doi:10.1117/12.2081204

20 March 2015 Locating seed points for automatic multi-organ segmentation using non-rigid registration and organ annotations

Ranveer R. Joyseeree, Henning Müller

Author Affiliations +

Proceedings Volume 9413, Medical Imaging 2015: Image Processing; 94133S (2015) https://doi.org/10.1117/12.2081204
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

Organ segmentation is helpful for decision-support in diagnostic medicine. Region-growing segmentation algorithms are popular but usually require that clinicians place seed points in structures manually. A method to automatically calculate the seed points for segmenting organs in three-dimensional (3D), non-annotated Computed Tomography (CT) and Magnetic Resonance (MR) volumes from the VISCERAL dataset is presented in this paper. It precludes the need for manual placement of seeds, thereby saving time. It also has the advantage of being a simple yet effective means of finding reliable seed points for segmentation. Affine registration followed by B-spline registration are used to align expert annotations of each organ of interest in order to build a probability map for their respective location in a chosen reference frame. The centroid of each is determined. The same registration framework as above is used to warp the calculated centroids onto the volumes to be segmented. Existing segmentation algorithms may then be applied with the mapped centroids as seed points and the warped probability maps as an aid to the stopping criteria for segmentation. The above method was tested on contrast{enhanced, thorax-abdomen CT images to see if calculated centroids lay within target organs, which would equate to successful segmentation if an effective segmentation algorithm were used. Promising results were obtained and are presented in this paper. The causes for observed failures were identified and countermeasures were proposed in order to achieve even better results in the next stage of development that will involve a wider variety of MR and CT images.

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Ranveer R. Joyseeree and Henning Müller "Locating seed points for automatic multi-organ segmentation using non-rigid registration and organ annotations", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94133S (20 March 2015); https://doi.org/10.1117/12.2081204

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KEYWORDS

Image segmentation

Liver

Computed tomography

Image registration

3D image processing

Detection and tracking algorithms

Kidney

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