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Brain imaging analysis on clinically acquired computed tomography (CT) isessential for the diagnosis, risk prediction of progression, and treatment ofthe structural phenotypes of traumatic brain injury (TBI). However, in realclinical imaging scenarios, entire body CT images (e.g., neck, abdomen, chest,pelvis) are typically captured along with whole brain CT scans. For instance,in a typical sample of clinical TBI imaging cohort, only ~15% of CT scansactually contain whole brain CT images suitable for volumetric brain analyses;the remaining are partial brain or non-brain images. Therefore, a manual imageretrieval process is typically required to isolate the whole brain CT scansfrom the entire cohort. However, the manual image retrieval is time andresource consuming and even more difficult for the larger cohorts. To alleviatethe manual efforts, in this paper we propose an automated 3D medical imageretrieval pipeline, called deep montage-based image retrieval (dMIR), whichperforms classification on 2D montage images via a deep convolutional neuralnetwork. The novelty of the proposed method for image processing is tocharacterize the medical image retrieval task based on the montage images. In acohort of 2000 clinically acquired TBI scans, 794 scans were used as trainingdata, 206 scans were used as validation data, and the remaining 1000 scans wereused as testing data. The proposed achieved accuracy=1.0, recall=1.0,precision=1.0, f1=1.0 for validation data, while achieved accuracy=0.988,recall=0.962, precision=0.962, f1=0.962 for testing data. Thus, the proposeddMIR is able to perform accurate CT whole brain image retrieval fromlarge-scale clinical cohorts.

Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network