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 height).

Methods: A clinical tool was developed to segment livers and spleen from 257 abdominal contrast-enhanced 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 female). 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 height) 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 (RMSE) was 2.3 mm, and the average surface distance (ASD) 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 (R2) with clinical/manual height measurements were 0.97 and 0.93 for the spleen and liver, respectively (p < 0.0001). No significant difference (p > 0.2) was found comparing interobserver and automatic-manual 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.

Figure 1

A schematic of the construction of the normalized probabilistic atlases of liver and spleen.

Figure 2

A schematic of the automated liver and spleen segmentation algorithm.

Figure 3

Normalized probabilistic atlases of the liver (a) and spleen (b) were created using a modified affine transformation: (i) Image of two organs before registration, (ii) after the modified affine registration; and (iii) the probabilistic atlas with a probability color map. Each atlas voxel contains probabilities associated with the presence of the liver or spleen.

Figure 4

An example of automated organ segmentation, liver, and spleen: (a) The patient image (I); (b) the smoothed data (I_s); (c) the conservative model (A) of organs overlaid on the patient data; (d) the mean model $\left(\overline{A}\right)$ of organs overlaid on the patient data; (e) the registered conservative model after the global affine registration (A_a) covering the patient liver∕spleen; (f) the registered mean model $\left({\overline{A}}_a\right)$ after the global affine registration; (g) the mean model after nonlinear registration $\left(\overline{A_r}\right)$; (h) the segmentation after GAC and adaptive convolution (L); and (i) the final segmentation after shape and location corrections (S).

Figure 5

Bland–Altman agreement plots for the linear estimations of liver height at MHL; from left to right we show the interobserver variability and the difference between manual (observers 1 and 2) and automatic (CAD) measurements.

Figure 6

Bland–Altman agreement plots for the linear estimations of spleen CC height; from left to right we show the interobserver variability and the difference between manual (observers 1 and 2) and automatic (CAD) measurements. The discrete 5 mm spaced steps are related to the slice thickness of image data.

Figure 7

An example of liver and spleen automatically segmented from a normal test case. 2D axial slices of the 3D CT data are shown.

Figure 8

Volume renderings of the segmentation of liver and spleen; (a) is a posterior view and (b) an anterior view. The liver and spleen ground truths are shown in dark colors with automated segmentation errors overlaid in light shades.

Figure 9

Three examples of segmentations of pathological, enlarged livers with unusual shapes from three different patients.

Figure 10

Examples from three different patients of segmentations of abnormal, enlarged spleens.

Figure 11

An example of pathological liver segmentation from the MICCAI data. 2D axial slices of the 3D CT data are shown.

Figure 12

Three examples of segmentation of livers from cases with partial hepatectomy from three different patients.