Deep convolutional networks for pancreas segmentation in CT imaging

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 state-of-the-art 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.

Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

“…Recently, deep CNNs have been applied to medical image analysis in several studies. Most of them have used deep CNNs for lesion detection or classification, while others have embedded CNNs into conventional organ‐segmentation processes to reduce the false positive rate in the segmentation results or to predict the likelihood of the image patches . Studies of this type usually divide CT images into numerous small 2D/3D patches at different locations, and then classify these patches into multiple predefined categories.…”

Extraction of target region in lung immunohistochemical image based on artificial neural network

“…Deep learning opens a new stage for Neural Network and is applied in image and speech recognition successfully in short time. As for biomedical image application, deep learning has been also well used in lung segmentation in chest radiographs [22], breast tissue segmentation [24], vertebral bodies segmentation in volumetric MR images [34], and pancreas segmentation in CT imaging [27].…”

Progress in Fully Automated Abdominal CT Interpretation

“…This makes automated image analysis of the pancreas extremely challenging. A number of different approaches have been taken to automated pancreas analysis, including the use of anatomic atlases, the location of the splenic and portal veins, and state-of-the-art computer science methods such as deep learning [8, 9, 35–41] (Fig. 1).…”