Deep convolutional networks for pancreas segmentation in CT imaging (1504.03967v1)

Abstract: Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to other segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve average Dice scores of 68%+-10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

• The paper introduces a fully automated ConvNet framework that refines initial superpixel labeling, boosting segmentation performance.
• It employs a hierarchical coarse-to-fine classification method using random forest probabilities and data augmentation for robust training.
• The method achieves an average Dice score of 68% ± 10%, marking a significant improvement over earlier techniques and rivaling state-of-the-art methods.

Deep Convolutional Networks for Pancreas Segmentation in CT Imaging: An Academic Overview

Automatic segmentation of organs in medical imaging is pivotal for advancing computer-aided diagnosis systems. The paper "Deep convolutional networks for pancreas segmentation in CT imaging" by Roth et al. addresses the challenge of accurately segmenting the pancreas—a task complicated by the organ's high anatomical variability and low contrast against surrounding tissues. The authors propose a fully automated method employing deep convolutional networks (ConvNets) to achieve this segmentation.

Methodological Approach

The research hinges on a hierarchical coarse-to-fine classification technique. Superpixels are initially extracted from CT images using Simple Linear Iterative Clustering (SLIC). A probabilistic map is generated using patch-level confidences from a cascade of random forest classifiers. Only superpixels with probabilities exceeding 0.5 are retained to serve as inputs to the ConvNet for further classification. The ConvNet assigns probabilities to these superpixel regions, distinguishing pancreatic tissue from non-pancreatic tissue.

The training leverages large annotated datasets and benefits from affordable parallel computing resources via GPUs. Superpixel classification is enhanced through data augmentation, employing scaling and non-rigid deformations to mitigate overfitting. ConvNet architecture is delineated with five layers of convolutional filters followed by max-pooling operations and fully connected neural networks. A final softmax layer types the tissue as either 'pancreas' or 'non-pancreas.'

Results and Performance Metrics

The method's efficacy was assessed on CT images from multiple patients, leading to a testing average Dice score of 68% ± 10%, with improvements over initial superpixel labeling methods. Although the initial labeling achieved a Dice score of only 27% ± 6%, the ConvNet method demonstrated substantial improvement post 3D Gaussian smoothing of ConvNet probabilities. The results are competitive with existing state-of-the-art pancreas segmentation methods, which show Dice scores from 46.6% to 68.8%. Notably, the approach does not rely on leave-one-out cross-validation, which is resource-intensive and less scalable.

Implications and Future Directions

The implications of successfully automating pancreas segmentation extend to enhancing the precision of CADx systems, notably for detecting pancreatic disorders such as cancer. The ConvNet's ability to classify superpixel regions suggests potential expansion to multi-organ segmentation frameworks.

This paper outlines a promising step toward leveraging ConvNets for medical imaging applications. Future research might explore multi-class ConvNet classification for simultaneous segmentation of various tissue types, thereby refining the accuracy and applicability of CAD systems. The potential scalability and adaptability of ConvNets could revolutionize segmentation tasks across medical imaging disciplines. By cultivating these capabilities, the research community moves closer to fully integrated, AI-enhanced diagnostic methodologies.