3D automatic anatomy recognition based on iterative graph-cut-ASM

Xinjian Chen; Jayaram K. Udupa; Ulas Bagci; Abass Alavi; Drew A. Torigian

doi:10.1117/12.844551

27 February 2010 3D automatic anatomy recognition based on iterative graph-cut-ASM

Xinjian Chen, Jayaram K. Udupa, Ulas Bagci, Abass Alavi, Drew A. Torigian

Proceedings Volume 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling; 76251T (2010) https://doi.org/10.1117/12.844551
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

We call the computerized assistive process of recognizing, delineating, and quantifying organs and tissue regions in medical imaging, occurring automatically during clinical image interpretation, automatic anatomy recognition (AAR). The AAR system we are developing includes five main parts: model building, object recognition, object delineation, pathology detection, and organ system quantification. In this paper, we focus on the delineation part. For the modeling part, we employ the active shape model (ASM) strategy. For recognition and delineation, we integrate several hybrid strategies of combining purely image based methods with ASM. In this paper, an iterative Graph-Cut ASM (IGCASM) method is proposed for object delineation. An algorithm called GC-ASM was presented at this symposium last year for object delineation in 2D images which attempted to combine synergistically ASM and GC. Here, we extend this method to 3D medical image delineation. The IGCASM method effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. We propose a new GC cost function, which effectively integrates the specific image information with the ASM shape model information. The proposed methods are tested on a clinical abdominal CT data set. The preliminary results show that: (a) it is feasible to explicitly bring prior 3D statistical shape information into the GC framework; (b) the 3D IGCASM delineation method improves on ASM and GC and can provide practical operational time on clinical images.

Citation Download Citation

Xinjian Chen, Jayaram K. Udupa, Ulas Bagci, Abass Alavi, and Drew A. Torigian "3D automatic anatomy recognition based on iterative graph-cut-ASM", Proc. SPIE 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 76251T (27 February 2010); https://doi.org/10.1117/12.844551

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