Deep Learning Based Rib Centerline Extraction and Labeling

Automated extraction and labeling of rib centerlines is a typically needed prerequisite for more advanced assisted reading tools that help the radiologist to efficiently inspect all 24 ribs in a CT volume. In this paper, we combine a deep learning-based rib detection with a dedicated centerline extraction algorithm applied to the detection result for the purpose of fast, robust and accurate rib centerline extraction and labeling from CT volumes. More specifically, we first apply a fully convolutional neural network (FCNN) to generate a probability map for detecting the first rib pair, the twelfth rib pair, and the collection of all intermediate ribs. In a second stage, a newly designed centerline extraction algorithm is applied to this multi-label probability map. Finally, the distinct detection of first and twelfth rib separately, allows to derive individual rib labels by simple sorting and counting the detected centerlines. We applied our method to CT volumes from 116 patients which included a variety of different challenges and achieved a centerline accuracy of 0.787 mm with respect to manual centerline annotations.

Towards radiologist-level cancer risk assessment in CT lung screening using deep learning

Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer combined. Recently, it has been demonstrated that screening those at high-risk for lung cancer low-dose computed tomography (CT) of the chest can significantly reduce this death rate. The process of evaluating a chest CT scan involves the identification of nodules that are contained within a scan as well as the evaluation of the likelihood that a nodule is malignant based on its imaging characteristics. This has motivated researchers to develop image analysis research tools, such as nodule detectors and nodule classifiers that can assist radiologists to make accurate assessments of the patient cancer risk. In this work, we propose a two-stage framework that can assess the lung cancer risk associated with a low-dose chest CT scan. At the first stage, our framework employs a nodule detector; while in the second stage, we use both the image area around the nodules and nodule features as inputs to a neural network that estimates the malignancy risk of the whole CT scan. The proposed approach: (a) has better performance than the PanCan Risk Model, a widely accepted method for cancer malignancy assessment, achieving around 7% better Area Under Curve score in two independent datasets we have employed; (b) has comparable performance to radiologists in estimating cancer risk at patient level; (c) employs a novel multi-instance weakly-labeled approach to train the deep learning network that requires confirmed cancer diagnosis only at the patient level (not at the nodule level); and (d) employs a large number of lung CT scans (more than 8000) from heterogeneous data sources (NLST, LHMC, and Kaggle competition data) to validate and compare model performance. AUC scores for our model, evaluated against confirmed cancer diagnosis, range between 82% to 90%.