Abstract
The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method.
Highlights
In recent years, liver ailments have been diagnosed as one of the common internal malignancies
We found that the attractiveness matrix, τ (∈ [0.001, 0.999]), derived with swarm intelligence variant ant colony optimization (ACO) (Section 3.4) as a quantitative index, is useful to adaptively regularize the graph-cut energy minimization
The proposed method has been evaluated on several clinical abdominal computed tomography (CT) volumes stored as DICOM images
Summary
Liver ailments have been diagnosed as one of the common internal malignancies. The typical clinical treatments for such ailments are living donor liver transplantation (LDLT), oncological liver sectioning, and chemotherapy. All these surgical treatments are quite intricate and vulnerable to life threatening complications. While all these procedures differ in procedural implementation, they all rely heavily on precise identification and segmentation of liver regions from contrast-enhanced 3D computed tomography (CTA) volumes, which provides detailed anatomical cues with a series of 2D image slices. Accurate segmentation of liver from surrounding tissues and organs on these 2D slices is necessary for measurement of [1] liver volume and vasculature analysis, which decides further treatment directions.
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