Morphological analysis of organs based on images is a key task in medical imaging computing. Several approaches have been proposed for the quantitative assessment of morphological changes, and they have been widely used for the analysis of the effects of aging, disease and other factors in organ morphology. In this work, we propose a deep neural network for predicting diabetes on abdominal shapes. The network directly operates on raw point clouds without requiring mesh processing or shape alignment. Instead of relying on hand-crafted shape descriptors, an optimal representation is learned in the end-to-end training stage of the network. For comparison, we extend the state-of-the-art shape descriptor BrainPrint to the AbdomenPrint. Our results demonstrate that the network learns shape representations that better separates healthy and diabetic individuals than traditional representations.
The use of shape models for the analysis of morphological changes associated with disease or other factors has mainly been explored in neuroimaging. Significant relationships between measurements of brain morphology and a variety of factors such as age @cite_5 and neurodegenerative diseases @cite_19 have been throughly explored. In this work, we focus on the abdominal organs liver and spleen. Previous approaches have explored the morphological analysis of the liver based on imaging data. Lamecker al @cite_23 present for the first time a statistical shape model of the liver. Dura al @cite_17 present the construction of a a probabilistic liver atlas. In terms of using shape models for the diagnosis of liver related diseases, Kohara al @cite_10 use a statistical shape model of the liver to assess differences between healthy subjects and individuals diagnosed with cirrhosis. A similar approach is proposed by Mukherjee al @cite_12 for the discrimination of chronic liver disease from CT Data and by Hori al @cite_7 where a statistical shape model is used to evaluate differences in liver shape caused by hepatic fibrosis.
@cite_7 @cite_19 @cite_23 @cite_5 @cite_10 @cite_12 @cite_17
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In computational anatomy, statistical shape model is used for quantitative evaluation of the variations of an organ shape. This paper is focused on construction of Statistical Shape Model of the liver and its application to computer assisted diagnosis. We prove the potential application of statistical shape models in classification of normal and cirrhosis livers. First, statistical shape model of liver is constructed. Then the coefficients of the model are used to recognize whether liver is normal or abnormal. 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 ( R 2 ) 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. We describe a method to capture disease-specific components in organ shapes. A statistical shape model, constructed by the principal component analysis (PCA) of organ shapes, is used to define the subspace representing inter-subject shape variability. The first PCA is applied to the datasets of healthy organ shapes to define the subspace of normal variability. Then, the datasets of diseased shapes are projected onto the orthogonal complement (OC) of the subspace of normal variability, and the second PCA is applied to the projected datasets to derive the subspace representing the disease-specific variability. To calculate the OC of an n-dimensional subspace, a novel closed-form formulation is developed. Experiments were performed to show that the support vector machine classification in the OC subspace better discriminated healthy and diseased liver shapes using 99 CT data. The effects of the number of training data and the difference in segmentation methods on the classification accuracy were evaluated to clarify the characteristics of the proposed method. This paper presents an automatic approach for segmentation of the liver from computer tomography (CT) images based on a 3D statistical shape model. Segmentation of the liver is an important prerequisite in liver surgery planning. One of the major challenges in building a 3D shape model from a training set of segmented instances of an object is the determination of the correspondence between different surfaces. We propose to use a geometric approach that is based on minimizing the distortion of the correspondence mapping between two different surfaces. For the adaption of the shape model to the image data a profile model based on the grey value appearance of the liver and its surrounding tissues in contrast enhanced CT data was developed. The robustness of this method results from a previous nonlinear diffusion filtering of the image data. Special focus is turned to the quantitative evaluation of the segmentation process. Several different error measures are discussed and implemented in a study involving more than 30 livers.