DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits previous segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans, using multi-level deep convolutional networks (ConvNets). We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels. We first present a dense labeling of local image patches via $P{-}\mathrm{ConvNet}$ and nearest neighbor fusion. Then we describe a regional ConvNet ($R_1{-}\mathrm{ConvNet}$) that samples a set of bounding boxes around each image superpixel at different scales of contexts in a "zoom-out" fashion. Our ConvNets learn to assign class probabilities for each superpixel region of being pancreas. Last, we study a stacked $R_2{-}\mathrm{ConvNet}$ leveraging the joint space of CT intensities and the $P{-}\mathrm{ConvNet}$ dense probability maps. Both 3D Gaussian smoothing and 2D conditional random fields are exploited as structured predictions for post-processing. We evaluate on CT images of 82 patients in 4-fold cross-validation. We achieve a Dice Similarity Coefficient of 83.6$\pm$6.3% in training and 71.8$\pm$10.7% in testing.Comment: To be presented at MICCAI 2015 - 18th International Conference on Medical Computing and Computer Assisted Interventions, Munich, German

2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both simple pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.Comment: This article will be presented at MICCAI (Medical Image Computing and Computer-Assisted Intervention) 201

Obesity and right ventricular structure and function: the MESA-RV study

Introduction: The relationship between obesity and right ventricle (RV) has been inadequately studied due to difficulty in imaging its more complex geometry by echocardiography. While obesity associated RV diastolic dysfunction has been shown, inconclusive data exists regarding systolic function. We aimed to determine the association between obesity and RV structure and function by cardiac magnetic resonance imaging (MRI) in a large multi-ethnic cohort. Purpose: We hypothesized that obesity would be associated with greater RV mass, larger RV end-diastolic volume (EDV), and lower RV ejection fraction (EF). Methods: Cardiac MRIs were analyzed from 1973 participants in the Multi-Ethnic Study of Atherosclerosis, which included individuals aged 45-84 years without clinical cardiovascular disease. Participants were divided into 3 categories based on BMI: normal (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2) and obese (= 30 kg/m2). Associations with RV measures were determined using multivariable regression. Results: The mean age was 62 ± 10 years, 47% were men. 43% were white, 28% African American, 20% Hispanic, and 9% Asian. In multivariable analyses adjusted for age, ethnicity, gender, cardiovascular risk factors and height, obesity was associated with higher RV mass, larger RVEDV (3.1 g/17% higher & 22.7 ml/20% higher respectively, p < 0.0001) and lower RVEF, mass/EDV ratio (-0.9%, p < 0.05; -8.1 mg/ml, p < 0.0001) as compared to normal BMI category participants. These findings persisted after adjusting for the respective left ventricle (LV) parameter. Within each BMI category, RV mass and EDV were positively associated with BMI while mass/EDV was negatively associated with BMI only in the normal BMI category Figures 1 and 2. Figure 1 Figure 2 Conclusion: In a cohort free of clinical cardiovascular disease, obesity was significantly associated with higher RV mass, RVEDV and lower RVEF even after adjustment for the LV. Future studies should examine the mechanism of this effect on the RV