The recent rapid success of deep convolutional neural networks (CNN) on many computer vision tasks largely benefits from the well-annotated Pascal VOC, ImageNet, and MS COCO datasets. However, it is challenging to get ImageNetlike annotations (1000 classes) in the medical imaging domain due to the lack of clinical training in the lay crowdsourcing community. We address this problem by presenting a semi-supervised training method for neural networks with true-class and pseudo-class (un-annotated class) labels on partially annotated training data. The true-class labels are supervised annotations from clinical professionals. The pseudo-class labels are unsupervised clustering of unannotated data. Our method rests upon the hypothesis of better coherent annotations with discriminative classes leading to better trained CNN models. We validated our method on extra-coronary calcification detection in low dose CT scans. The CNN trained with true-class and 10 pseudo-classes achieved a 78.0% sensitivity at 10 false positives per scan (0.3 false positive per slice), which significantly outperformed the CNN trained with true-class only (sensitivity=25.0% at 10 false positives per patient).
Pericardial effusion on CT scans demonstrates very high shape and volume variability and very low contrast to adjacent structures. This inhibits traditional automated segmentation methods from achieving high accuracies. Deep neural networks have been widely used for image segmentation in CT scans. In this work, we present a two-stage method for pericardial effusion localization and segmentation. For the first step, we localize the pericardial area from the entire CT volume, providing a reliable bounding box for the more refined segmentation step. A coarse-scaled holistically-nested convolutional networks (HNN) model is trained on entire CT volume. The resulting HNN per-pixel probability maps are then threshold to produce a bounding box covering the pericardial area. For the second step, a fine-scaled HNN model is trained only on the bounding box region for effusion segmentation to reduce the background distraction. Quantitative evaluation is performed on a dataset of 25 CT scans of patient (1206 images) with pericardial effusion. The segmentation accuracy of our two-stage method, measured by Dice Similarity Coefficient (DSC), is 75.59±12.04%, which is significantly better than the segmentation accuracy (62.74±15.20%) of only using the coarse-scaled HNN model.
Artery calcification is observed commonly in elderly patients, especially in patients with chronic kidney disease, and may affect coronary, carotid and peripheral arteries. Vascular calcification has been associated with many clinical outcomes. Manual identification of calcification in CT scans requires substantial expert interaction, which makes it time-consuming and infeasible for large-scale studies. Many works have been proposed for coronary artery calcification detection in cardiac CT scans. In these works, coronary artery extraction is commonly required for calcification detection. However, there are few works about abdominal or pelvic artery calcification detection. In this work, we present a method for automatic pelvic artery calcification detection on CT scan. This method uses the recent advanced faster region-based convolutional neural network (R-CNN) to directly identify artery calcification without a need for artery extraction since pelvic artery extraction itself is challenging. Our method first generates category-independent region proposals for each slice of the input CT scan using region proposal networks (RPN). Then, each region proposal is jointly classified and refined by softmax classifier and bounding box regressor. We applied the detection method to 500 images from 20 CT scans of patients for evaluation. The detection system achieved a 77.4% average precision and a 85% sensitivity at 1 false positive per image.