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.
Shape analysis of the spleen is a far less explored area of research. Tateyama @cite_4 al present the use of a Point Distribution Model (PDM) for the analysis of spleen shape. Yates al @cite_22 present a morphological study of the spleen, relating the principal components obtained from a statistical shape model to anthropometric and demographic information.
@cite_4 @cite_22
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Statistical shape models are an effective way to create computational models of human organs that can incorporate inter-subject geometrical variation. The main objective of this study was to create statistical mean and boundary models of the human spleen in an occupant posture. Principal component analysis was applied to fifteen human spleens in order to find the statistical modes of variation, mean shape, and boundary models. A landmark sliding approach was utilized to refine the landmarks to obtain a better shape correspondence and create a better representation of the underlying shape contour. The first mode of variation was found to be the overall volume, and it accounted for 69 of the total variation. The mean model and boundary models could be used to develop probabilistic finite element (FE) models which may identify the risk of spleen injury during vehicle collisions and consequently help to improve automobile safety systems. 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. Statistical shape model (SSM) is to model the shape variation of an object. The statistical shape models are constructed by analysis of the positions of a set of landmark points based and use the surface information. In this paper, we propose a new PCA based statistical shape modeling technique and its application to medical applications. In the proposed method, boundary points of each slice are used as surface points and distance of these points to the corresponding slice center are used to represent the shape instead of their coordinates. Spleen volume estimation using automated image segmentation technique may be used to detect splenomegaly (abnormally enlarged spleen) on Magnetic Resonance Imaging (MRI) scans. In recent years, Deep Convolutional Neural Networks (DCNN) segmentation methods have demonstrated advantages for abdominal organ segmentation. However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods. In this paper, we propose the Splenomegaly Segmentation Network (SSNet) to address spatial variations when segmenting extraordinarily large spleens. SSNet was designed based on the framework of image-to-image conditional generative adversarial networks (cGAN). Specifically, the Global Convolutional Network (GCN) was used as the generator to reduce false negatives, while the Markovian discriminator (PatchGAN) was used to alleviate false positives. A cohort of clinically acquired 3D MRI scans (both T1 weighted and T2 weighted) from patients with splenomegaly were used to train and test the networks. The experimental results demonstrated that a mean Dice coefficient of 0.9260 and a median Dice coefficient of 0.9262 using SSNet on independently tested MRI volumes of patients with splenomegaly.