Atlas-Based Automated Segmentation of Spleen and Liver Using Adaptive Enhancement Estimation

Abstract

Abstract. The paper presents the automated segmentation of spleen and liver from contrast-enhanced CT images of normal and hepato/splenomegaly populations. The method used 4 steps: (i) a mean organ model was registered to the patient CT; (ii) the first estimates of the organs were improved by a geodesic active contour; (iii) the contrast enhancements of liver and spleen were estimated to adjust to patient image characteristics, and an adaptive convolution refined the segmentations; (iv) lastly, a normalized probabilistic atlas corrected for shape and location for the precise computation of each organ's volume and height (mid-hepatic liver height and cephalocaudal spleen height). Results from test data demonstrated the method's ability to accurately segment the spleen (RMS error = 1.09mm; DICE/Tanimoto overlaps = 95.2/91) and liver (RMS error = 2.3mm, and DICE/Tanimoto overlaps = 96.2/92.7). The correlations (R 2 ) with clinical/manual height measurements were 0.97 and 0.93 for the spleen and liver respectively.

Multi-Organ Segmentation with Missing Organs in Abdominal CT Images

“…This method employs the MAP estimation of organ label l ∈ {1, .., L} over 4D spatio-intensity feature vector v = (x, y, z, I(x, y, z)):l = argmax l p(v|l)p(l). The prior p(l) is modeled by a standard probabilistic atlas [2,9]. The atlas A l (x) ∈ [0, 1], x = (x, y, z), is built by registering K training images of normal anatomy to a fixed reference image I R with a size-preserving affine registration then computing a probability map for each of L modeled organs by counting manually segmented organs.…”

“…Multi-organ segmentation (MOS) has recently become popular toward improving overall segmentation accuracy when segmenting a set of organs located nearby, enabling comprehensive computer-aided diagnosis (CAD) of various multi-focal abdominal diseases [1][2][3][4][5][6][7][8][9][10]. In this paper, we investigate how such MOS can be extended to a patient population with missing organs due to surgical resections.…”

“…A common process in various MOS methods is to fit an atlas of normal organ anatomy to an image to be analyzed. When analyzing a case with missing organs, regardless of atlas formats (i.e., static [3], probabilistic [2,4,5,8,9], or geometric [4,[6][7][8]10]), MOS can fail to segment other intact organs because of 1) mis-match of the atlas' part corresponding to the missing organs to nearby non-targets and 2) post-surgical organ shifts. Fig.1(a) illustrates such a failure case with a missing right kidney where the liver (red) shifted downward into the cavity caused by the removed kidney and a part of the liver was incorrectly identified as kidney (cyan).…”

Identification of Structures in Medical Images

“…Recent works in this area have also been described by Ruskó et al (2009), Masuda et al (2010 and Pu et al (2009). Linguraru et al (2009) proposed a method to isolate both the spleen and liver in contrast-enhanced CT images by first aligning the images with models from an atlas and then improving the results by an active contour technique.…”

Hierarchical patch generation for multilevel statistical shape analysis by principal factor analysis decomposition

“…Statistical atlases of the abdomen represent useful tools for the initialization of segmentation of organs [7]. In a more comprehensive manner, multi-level statistical shape models [9] have been proposed to allow better shape representation of complex structures, and to add more flexibility in segmentation procedures.…”