Multi-object active shape model construction for abdomen segmentation: Preliminary results

Abstract

The automatic segmentation of abdominal organs is a pre-requisite for many medical applications. Successful methods typically rely on prior knowledge about the to be segmented anatomy as it is for instance provided by means of active shape models (ASMs). Contrary to most previous ASM based methods, this work does not focus on individual organs. Instead, a more holistic approach that aims at exploiting inter-organ relationships to eventually segment a complex of organs is proposed. Accordingly, a flexible framework for automatic construction of multi-object ASMs is introduced, employed for coupled shape modeling, and used for co-segmentation of liver and spleen based on a new coupled shape/separate pose approach. Our first results indicate feasible segmentation accuracies, whereas pose decoupling leads to substantially better segmentation results and performs in average also slightly better than the standard single-object ASM approach.

Computational anatomy for multi-organ analysis in medical imaging: A review

“…All of these studies demonstrated that even the high level of global modeling of multi-organs is advantageous over individually modelling each organ separately. More recent studies have also shown that the co-modeling of organs like liver and spleen, known to be highly variable and not directly connected, can improve the segmentation of these organs as compared to individual models (Gollmer et al, 2012). However, these simple global models do not embed any inter-organ relations; more sophisticated models inspired from anatomy and physiology are presented in the following sections.…”

Image Processing Tool Promoting Decision-Making in Liver Surgery of Patients with Chronic Kidney Disease

“…Algorithms used for liver segmentation include grey level evaluation [6,11,[24][25][26][27], clustering [17,[28][29][30], region-based method [31,32], Snakesbased method [33], grow-cut [34], graph cuts [15,[35][36][37], level set [16,[38][39][40][41], combinations of different approaches as for example Snakes and grow-cut [33], or graph cut and gradient flow active contour [5], or morphological operations and graph cuts [9,42], grey level and a priori knowledge like CT numbers and location [25], hidden Markov measure field model [18], multi-class smoothed Bayesian classification [20,21], and edge based methods [43]. The use of segmentation algorithm based on priority knowledge about appearance, shape and size of the liver [10,23,[44][45][46][47][48][49][50][51][52] and methods based on neural networks [53,54] have been also proposed.…”