Convolutional Neural Networks Enable Robust Automatic Segmentation of the Rat Hippocampus in MRI After Traumatic Brain Injury

Abstract

Registration-based methods are commonly used in the automatic segmentation of magnetic resonance (MR) brain images. However, these methods are not robust to the presence of gross pathologies that can alter the brain anatomy and affect the alignment of the atlas image with the target image. In this work, we develop a robust algorithm, MU-Net-R, for automatic segmentation of the normal and injured rat hippocampus based on an ensemble of U-net-like Convolutional Neural Networks (CNNs). MU-Net-R was trained on manually segmented MR images of sham-operated rats and rats with traumatic brain injury (TBI) by lateral fluid percussion. The performance of MU-Net-R was quantitatively compared with methods based on single and multi-atlas registration using MR images from two large preclinical cohorts. Automatic segmentations using MU-Net-R and multi-atlas registration were of excellent quality, achieving cross-validated Dice scores above 0.90 despite the presence of brain lesions, atrophy, and ventricular enlargement. In contrast, the performance of single-atlas segmentation was unsatisfactory (cross-validated Dice scores below 0.85). Interestingly, the registration-based methods were better at segmenting the contralateral than the ipsilateral hippocampus, whereas MU-Net-R segmented the contralateral and ipsilateral hippocampus equally well. We assessed the progression of hippocampal damage after TBI by using our automatic segmentation tool. Our data show that the presence of TBI, time after TBI, and whether the hippocampus was ipsilateral or contralateral to the injury were the parameters that explained hippocampal volume.

Hippocampal position and orientation as prognostic biomarkers for posttraumatic epileptogenesis: An experimental study in a rat lateral fluid percussion model

“…We segmented the brain mask and both the ipsilateral and contralateral hippocampus using MU‐Net‐R, which is a CNN designed for the segmentation of rat brain MRI 22,28 . MU‐Net‐R displayed segmentation performance of the hippocampus comparable with that of human raters on both datasets, with Dice overlap scores 29 of .92 on EpiBioS4Rx and .83 on EPITARGET 22 …”

“…• Using a geometric construction, 39 hippocampal parameters describing the orientation and relative positioning of the hippocampi were extracted • Training random forest classifiers, we accurately discriminated between sham-operated and TBI rats from 2 days to 5 months postsurgery • At 5 months postsurgery, epileptic and nonepileptic animals were distinguished based on hippocampal geometry with 80% accuracy • We visualize the most discriminating changes between epileptic and nonepileptic rats Without Walls project Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). We automatically segmented all samples using MU-Net-R, 22 a convolutional neural network (CNN), 23 which reduces bias for healthy anatomy compared to atlas registration-based segmentation methods. Using a geometric construction, we then extracted 39 anatomical parameters for each animal at each timepoint and analyzed their effectiveness in discriminating between (1) sham-operated experimental control and TBI rats and (2) TBI rats with (TBI+) and without (TBI−) epilepsy.…”

“…We segmented the brain mask and both the ipsilateral and contralateral hippocampus using MU-Net-R, which is a CNN designed for the segmentation of rat brain MRI. 22,28 MU-Net-R displayed segmentation performance of the hippocampus comparable with that of human raters on both datasets, with Dice overlap scores 29 We defined the plane P 1 as an interpolating plane, minimizing the sum of distances of each voxel in the hippocampus segmentation mask H from the plane. We fitted the best interpolating plane using singular value decomposition, implemented with scikit-spatial (https://sciki tspati al.readt hedocs.io/), and described the plane through the point p 1 p and the normal vector p 1 n .…”

“…We utilized two large MRI datasets of rats with lateral FPI, one generated in the European Union‐funded study EPITARGET and another in the National Institutes of Health‐funded Centers Without Walls project Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). We automatically segmented all samples using MU‐Net‐R, 22 a convolutional neural network (CNN), 23 which reduces bias for healthy anatomy compared to atlas registration‐based segmentation methods. Using a geometric construction, we then extracted 39 anatomical parameters for each animal at each timepoint and analyzed their effectiveness in discriminating between (1) sham‐operated experimental control and TBI rats and (2) TBI rats with (TBI+) and without (TBI−) epilepsy.…”

MRI-Guided Electrode Implantation for Chronic Intracerebral Recordings in a Rat Model of Post−Traumatic Epilepsy—Challenges and Gains

“…Like in humans with PTE, progressive brain damage involves epileptogenic regions, including the cerebral cortex and hippocampus [ 7 , 19 , 20 ]. The progression of cortical and hippocampal atrophy and spatial distortion, however, varies significantly between animals over time [ 7 , 21 , 22 , 23 ].…”