  Brain imaging analysis on clinically acquired computed tomography (CT) is
essential for the diagnosis, risk prediction of progression, and treatment of
the structural phenotypes of traumatic brain injury (TBI). However, in real
clinical 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 scans
actually contain whole brain CT images suitable for volumetric brain analyses;
the remaining are partial brain or non-brain images. Therefore, a manual image
retrieval process is typically required to isolate the whole brain CT scans
from the entire cohort. However, the manual image retrieval is time and
resource consuming and even more difficult for the larger cohorts. To alleviate
the manual efforts, in this paper we propose an automated 3D medical image
retrieval pipeline, called deep montage-based image retrieval (dMIR), which
performs classification on 2D montage images via a deep convolutional neural
network. The novelty of the proposed method for image processing is to
characterize the medical image retrieval task based on the montage images. In a
cohort of 2000 clinically acquired TBI scans, 794 scans were used as training
data, 206 scans were used as validation data, and the remaining 1000 scans were
used 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 proposed
dMIR is able to perform accurate CT whole brain image retrieval from
large-scale clinical cohorts.
