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Automated detection of sclerotic metastases (bone lesions) in ComputedTomography (CT) images has potential to be an important tool in clinicalpractice and research. State-of-the-art methods show performance of 79%sensitivity or true-positive (TP) rate, at 10 false-positives (FP) per volume.We design a two-tiered coarse-to-fine cascade framework to first operate ahighly sensitive candidate generation system at a maximum sensitivity of ~92%but with high FP level (~50 per patient). Regions of interest (ROI) for lesioncandidates are generated in this step and function as input for the secondtier. In the second tier we generate N 2D views, via scale, randomtranslations, and rotations with respect to each ROI centroid coordinates.These random views are used to train a deep Convolutional Neural Network (CNN)classifier. In testing, the CNN is employed to assign individual probabilitiesfor a new set of N random views that are averaged at each ROI to compute afinal per-candidate classification probability. This second tier behaves as ahighly selective process to reject difficult false positives while preservinghigh sensitivities. We validate the approach on CT images of 59 patients (49with sclerotic metastases and 10 normal controls). The proposed method reducesthe number of FP/vol. from 4 to 1.2, 7 to 3, and 12 to 9.5 when comparing asensitivity rates of 60%, 70%, and 80% respectively in testing. TheArea-Under-the-Curve (AUC) is 0.834. The results show marked improvement uponprevious work.

Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications