Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation

Abstract

Purpose:To investigate the potential of the normalized probabilistic atlases and computer-aided medical image analysis to automatically segment and quantify livers and spleens for extracting imaging biomarkers ͑volume and height͒. Methods: A clinical tool was developed to segment livers and spleen from 257 abdominal contrastenhanced CT studies. There were 51 normal livers, 44 normal spleens, 128 splenomegaly, 59 hepatomegaly, and 23 partial hepatectomy cases. 20 more contrast-enhanced CT scans from a public site with manual segmentations of mainly pathological livers were used to test the method. Data were acquired on a variety of scanners from different manufacturers and at varying resolution. Probabilistic atlases of livers and spleens were created using manually segmented data from ten noncontrast CT scans ͑five male and five female͒. The organ locations were modeled in the physical space and normalized to the position of an anatomical landmark, the xiphoid. The construction and exploitation of liver and spleen atlases enabled the automated quantifications of liver/spleen volumes and heights ͑midhepatic liver height and cephalocaudal spleen height͒ from abdominal CT data. The quantification was improved incrementally by a geodesic active contour, patient specific contrast-enhancement characteristics passed to an adaptive convolution, and correction for shape and location errors. Results: The livers and spleens were robustly segmented from normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 96.2%/92.7%, the volume/height errors were 2.2%/2.8%, the root-mean-squared error ͑RMSE͒ was 2.3 mm, and the average surface distance ͑ASD͒ was 1.2 mm. The spleen quantification led to 95.2%/91% Dice/Tanimoto overlaps, 3.3%/ 1.7% volume/height errors, 1.1 mm RMSE, and 0.7 ASD. The correlations ͑R 2 ͒ with clinical/ manual height measurements were 0.97 and 0.93 for the spleen and liver, respectively ͑p Ͻ 0.0001͒. No significant difference ͑p Ͼ 0.2͒ was found comparing interobserver and automaticmanual volume/height errors for liver and spleen. Conclusions: The algorithm is robust to segmenting normal and enlarged spleens and livers, and in the presence of tumors and large morphological changes due to partial hepatectomy. Imaging biomarkers of the liver and spleen from automated computer-assisted tools have the potential to assist the diagnosis of abdominal disorders from routine analysis of clinical data and guide clinical management.

Three Dimensional Projection Environment for Molecular Design and Surgical Simulation

“…Segmentation is an essential part of visualizing the patient in 3D, which is typically done manually. Automating this time intensive method has been attempted for specific organs such as the liver [44,45], breast [46], bladder, lungs [47], or blood vessels [48]. Not all targets are easily segmented through automation.…”

Liver Segmentation and Volume Estimation from Preoperative CT Images in Hepatic Surgical Planning: Application of a Semiautomatic Method Based on 3D Level Sets

“…Recently, most of new liver segmentation methods combine different techniques: statistical shape models, mathematical morphology and Level Set approaches. (Linguraru et al, 2010) present a clinical tool developed to segment liver and spleen based on probabilistic atlases. The atlases are created using manually segmented data from non contrast CT images.…”

Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans

“…In addition some of these methods also require initialization. As examples, active contour segmentation methods needs curve initialization; region growing segmentation methods need seed point initialization, etc (Foo 2006 (Linguraru, Sandberg et al 2010). But it is noted that abdominal images segmentation is complex and challenging task due to several reasons contributed by high similarities in the gray levels among different structures, the surrounding soft tissues as well as inhomogeneity in shape and texture of the same structure in different image slices (Ding, Leow et al 2005).…”