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

Abstract

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-ConvNet and nearest neighbor fusion. Then we describe a regional ConvNet (R 1 −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 −ConvNet leveraging the joint space of CT intensities and the P −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 crossvalidation. We achieve a Dice Similarity Coefficient of 83.6±6.3% in training and 71.8±10.7% in testing.

A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

“…The last two lines show two upper-bounds of our approach, i.e., "Best of All Iterations" means that we choose the highest DSC value over 10 iterations, and "Oracle Bounding Box" corresponds to using the ground-truth segmentation to generate the bounding box in testing. We also compare our results with the state-ofthe-art [9][8], demonstrating our advantage over all statistics.…”

“…We evaluate our approach on the NIH pancreas segmentation dataset [9], which contains 82 contrast-enhanced abdominal CT volumes. The resolution of each CT scan is 512 × 512 × L, where L ∈ [181, 466] is the number of sampling slices along the long axis of the body.…”

“…The difficulty mainly comes from the high anatomical variability and/or the small volume of the target organs. Indeed researchers sometimes design a specific segmentation approach for each organ [1] [9].…”

“…Among different abdominal organs, pancreas segmentation is especially difficult, as the target often suffers from high variability in shape, size and location [9], while occupying only a very small fraction (e.g., < 0.5%) of the entire CT volume. In such cases, deep neural networks can be disrupted by the background region, which occupies a large fraction of the input volume and includes complex and variable contents.…”

Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks

“…Roth et al [4] apply multi-level deep CNN models for dense image pixel labeling, conducting pancreas segmentation gradually from coarse to fine representation. However, pixel or superpixel-wise prediction of deep networks is very inefficient since it requires thousands of inferences for a testing image.…”