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 $P-\mathrm{ConvNet}$ 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.

To be presented at MICCAI 2015 - 18th International Conference on Medical Computing and Computer Assisted Interventions, Munich, Germany

References in corpus (2)

• Feedforward semantic segmentation with zoom-out features
• A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans

Cited by in corpus (28)

• A review: Deep learning for medical image segmentation using multi-modality fusion
• Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
• Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems
• Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities
• An application of cascaded 3D fully convolutional networks for medical image segmentation
• Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
• Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
• Radiological images and machine learning: trends, perspectives, and prospects
• Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
• Reinventing 2D Convolutions for 3D Images
• A Method of Rapid Quantification of Patient-Specific Organ Dose for CT Using Coupled Deep-Learning based Multi-Organ Segmentation and GPU-accelerated Monte Carlo Dose Computing
• Multi-level Attention Model for Weakly Supervised Audio Classification
• Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?
• TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions
• Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction
• Pancreas Segmentation in CT and MRI Images via Domain Specific Network Designing and Recurrent Neural Contextual Learning
• Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans
• Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
• A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation
• A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
• Near Real-time Hippocampus Segmentation Using Patch-based Canonical Neural Network
• Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation
• Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives
• Imperfect Segmentation Labels: How Much Do They Matter?
• Brain Abnormality Detection by Deep Convolutional Neural Network
• Outlier Guided Optimization of Abdominal Segmentation
• Body Part Regression for CT Images
• MCMC Guided CNN Training and Segmentation for Pancreas Extraction