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


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015

AUTHORS

Holger R. Roth , Jianhua Yao , Le Lu , James Stieger , Joseph E. Burns , Ronald M. Summers

ABSTRACT

Automated detection of sclerotic metastases (bone lesions) in Computed Tomography (CT) images has potential to be an important tool in clinical practice 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 a highly sensitive candidate generation system at a maximum sensitivity of \(\sim \)92 % but with high FP level (\(\sim \)50 per patient). Regions of interest (ROI) for lesion candidates are generated in this step and function as input for the second tier. In the second tier we generate \(N\) 2D views, via scale, random translations, 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 probabilities for a new set of \(N\) random views that are averaged at each ROI to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. We validate the approach on CT images of 59 patients (49 with sclerotic metastases and 10 normal controls). The proposed method reduces the number of FP/vol. from 4 to 1.2, 7 to 3, and 12 to 9.5 when comparing a sensitivity rates of 60, 70, and 80 % respectively in testing. The Area-Under-the-Curve (AUC) is 0.834. The results show marked improvement upon previous work. More... »

PAGES

3-12

Book

TITLE

Recent Advances in Computational Methods and Clinical Applications for Spine Imaging

ISBN

978-3-319-14147-3
978-3-319-14148-0

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-14148-0_1

DOI

http://dx.doi.org/10.1007/978-3-319-14148-0_1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008222279


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