Automated metastatic brain lesion detection: a computer aided diagnostic and clinical research tool

Jeremy Devine; Arjun Sahgal; Irene Karam; Anne L. Martel

doi:10.1117/12.2217121

24 March 2016 Automated metastatic brain lesion detection: a computer aided diagnostic and clinical research tool

Jeremy Devine, Arjun Sahgal, Irene Karam, Anne L. Martel

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97852K (2016) https://doi.org/10.1117/12.2217121
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

The accurate localization of brain metastases in magnetic resonance (MR) images is crucial for patients undergoing stereotactic radiosurgery (SRS) to ensure that all neoplastic foci are targeted. Computer automated tumor localization and analysis can improve both of these tasks by eliminating inter and intra-observer variations during the MR image reading process. Lesion localization is accomplished using adaptive thresholding to extract enhancing objects. Each enhancing object is represented as a vector of features which includes information on object size, symmetry, position, shape, and context. These vectors are then used to train a random forest classifier. We trained and tested the image analysis pipeline on 3D axial contrast-enhanced MR images with the intention of localizing the brain metastases. In our cross validation study and at the most effective algorithm operating point, we were able to identify 90% of the lesions at a precision rate of 60%.

Citation Download Citation

Jeremy Devine, Arjun Sahgal, Irene Karam, and Anne L. Martel "Automated metastatic brain lesion detection: a computer aided diagnostic and clinical research tool", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852K (24 March 2016); https://doi.org/10.1117/12.2217121

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