Interdependent 3D Segmentation of Abdominal Organs in MRI Data Using Multiple Level Set Methods in Organ-Specific Probability Maps

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Segmentation of abdominal organs in medical image data offers miscellaneous possibilities to advance diagnostic and epidemiological standards and is therefore of particular interest to accelerate modern medical research. This project proposal is designed for 3D segmentation in an epidemiological study, in which more than 3500 magnetic resonance (MR) datasets from volunteers are produced including the upper abdominal body part. Manual segmentation is very elaborate and time-consuming for such high amount of MR volume data requiring (fully) automatic segmentation support. Although fully automatic 3D segmentation in MR volume data is very challenging, the particular motivation of this project is to develop a highly valuable fully automatic segmentation framework that can segment several abdominal organs simultaneously.Multi-organ segmentation approaches segment adjacent organs simultaneously in a combined delineation strategy and can therefore reduce overspills more effectively than single-organ segmentation methods. However, existing multi-organ approaches are designed for CT data segmentation and have severe drawbacks. We propose novel, automatic methods for MR volume data segmentation that will use prior knowledge of organ-specific features and incorporate them in extended algorithms in a hierarchically organized multi-stepped 3D segmentation framework.In previous work we showed that probability maps (PMs) support segmentation of single target organs efficiently and we will extend this successful concept for multi-organ segmentation using delineation-relevant organ features. Since some abdominal organs show locally varying MR intensity distributions, we will take inner-organ locations into account. Highly performant machine learning techniques incorporating relevant organ features will be applied to generate meaningful PMs. We will consider particular requirements of epidemiological studies like tissue type adapted organ segmentation (e.g. for fatty livers) and detection of potentially removed organs due to surgical operations.A multiple level set method will be applied in organ-specific probability maps to segment several target organs simultaneously according to an interdependent segmentation strategy. Each zero level set delineates an organ and is controlled by minimized organ-specific energy terms. Novel 3D shape descriptors will be used to determine exact organ shape distributions that will be used in the prior shape-based level set segmentation part. Our modularized segmentation framework will be hierarchically organized for liver, renal parenchyma and spleen segmentation in the first level and gallbladder and pancreas segmentation in the second level. Though, it can be flexibly extended for future use and will also be applicable and therefor highly valuable for segmentation in clinical routines.