DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation (1506.06448v1)

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.

• The paper presents a hierarchical deep ConvNet framework that refines segmentation from coarse to fine, substantially improving pancreas delineation.
• It integrates P-ConvNet for dense patch labeling, R1-ConvNet for regional analysis, and R2-ConvNet with 3D smoothing and 2D CRFs to boost Dice Similarity Coefficient performance.
• The approach establishes a scalable foundation for multi-organ segmentation and potential real-time CAD applications in medical imaging.

Automated Pancreas Segmentation Using Multi-level Deep Convolutional Networks

The paper "DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation" presents a sophisticated approach to the automatic segmentation of the pancreas in abdominal CT scans using deep convolutional networks (ConvNets). This is an area of significant interest due to the inherent challenges posed by the pancreas's anatomical variability. The authors propose a probabilistic bottom-up methodology employing hierarchical, coarse-to-fine classification across image levels, which innovatively surpasses previous pancreas segmentation methods.

Methodological Overview

The paper introduces a multi-level deep ConvNet architecture, which aims to enhance segmentation precision progressively. The approach begins with a dense labeling of local image patches using P-ConvNet, combined with nearest neighbor fusion for initial probability estimation. This is followed by a regional ConvNet (R1-ConvNet) that processes superpixel regions containing bounding boxes of varying scales, refining the segmentation probabilities through a zoom-out mechanism. Finally, a stacked R2-ConvNet is employed, integrating both image intensities and probability maps from P-ConvNet to further enhance segmentation accuracy.

Post-processing steps such as 3D Gaussian smoothing and 2D conditional random fields (CRFs) are utilized to provide structured predictions, refining the resultant segmentation.

Results and Performance

The authors evaluated their approach on CT images of a specific patient cohort using a 4-fold cross-validation technique. The methodology resulted in an impressive Dice Similarity Coefficient (DSC) improvement, showing mean performance metrics that are notably superior to previous reports in the literature. Specifically, the highest DSC achieved in testing was reported to be the most significant in this domain, indicating the method's efficacy in tackling the challenge of pancreas segmentation.

Critical evaluation metrics indicated that while traditional candidate generation started with an average DSC of approximately 26.1%, the application of multi-level ConvNet models, particularly the R2-ConvNet leveraging 3D Gaussian smoothing, substantially increased the average DSC.

Implications and Future Work

The paper's approach not only optimizes pancreas segmentation but also lays the groundwork for scalable integration into multi-organ segmentation systems. Given the ConvNet's adaptability for multi-class classification, this methodology can potentially extend to other organs and lesions, including varied anatomical structures and pathologies such as tumors.

The key contribution of this research is its demonstration of how deep learning frameworks, particularly multi-level ConvNets, can address significant variability challenges within medical imaging. Future developments may include the expansion to larger datasets, incorporation into real-time CAD systems, and refinement through additional augmentation techniques or architectures to further improve generalization and processing speeds.

In conclusion, the paper details a comprehensive methodology that significantly advances the state-of-the-art in automated pancreas segmentation and contributes practical and theoretical insights to the field of medical image analysis. The innovative use of ConvNets presents a pathway to tackle similar challenges across diverse medical imaging scenarios.