Finding Seed Points For Organ Segmentation Using Example Annotations

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Organ segmentation is important in diagnostic medicine to make current decision support tools more eff ective and efficient. Performing it automatically can save time and labor. In this paper, a method to perform automatic identi cation 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.

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Finding Seed Points For Organ Segmentation Using Example Annotations

  1. 1. Finding Seed Points For Organ Segmentation Using Example Annotations Ranveer 1,2, Joyseeree Henning 1,3 Müller 1University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland; 2Eidgenössiche Technische Hochschule (ETH), Zürich, Switzerland; 3Medical Informatics, University Hospitals and University of Geneva, Switzerland. Summary 1. A fully-automatic method to find the starting point for the region-growing segmentation of organs of interest is presented. 2. Annotations for MR/CT volumes are registered to create 3D probability maps for organ location on a reference frame. 3. The centroids of the maps are calculated and used as seed points for segmentation. Introduction • Organ segmentation is vital in diagnostic medicine • Manual delineation by experts is time-consuming • Automatic organ segmentation has the following benefits: • Clinicians’ workload can be reduced • Time saved can be reallocated to patient care Results • Probability maps • e.g for the liver, the colour range from blue to dark red corresponds to increasing probability for a voxel to lie within the organ: Methods • Computed centroids • Registration: • • Affine (chosen as a compromise) Dark red dot on the coloured probability maps: • More accurate and time-consuming than rigid registration but less accurate and faster than non-rigid registration • Mattes Mutual Information (chosen as a compromise) • Fast implementation of standard mutual information • Better suited for multimodal applications than correlation-based methods • Creation of probability maps: • • • • • Z = Organ of interest PDZ = Probability distribution of organ Z N = Number of training 3D MR/CT volumes Yn = Training volume n AT(Yn,Z) = Transformed annotation of organ Z corresponding to Yn • Evaluation • For a series of 7 reference volumes, whether the centroid lies within the target organ is investigated. The outcome is shown below: • A simple region growing segmentation algorithm is implemented and used to demonstrate the effectiveness of identified centroids lying within target organs: • Generation of centroid that is used as seed point • Centroid [xc ,yc ,zc] of an MxNxP volume is the weighted average location of a point within PDz and is calculated using V(x,y,z) which represent voxel values in PDz: • Testing • A simple region-growing segmentation algorithm is implemented to test if segmentation can be carried out automatically • Evaluation • • Whether calculated centroid lies within target organ on reference image is tested Dice coefficient is used to gauge extent of overlap between segmentation result and reference annotation Dice = 0.884 Contact and more information: Ranveer.joyseeree@hevs.ch, http://iig.hevs.ch/ Dice = 0.969 Dice = 0.972

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