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

Frontiers in Neurology

Eppu Manninen¹  Asla Pitkänen¹  Riikka Immonen¹  Xavier Ekolle Ndode-Ekane¹  Olli Gröhn¹  Juan Miguel Valverde¹  Elina Hämäläinen¹  Jussi Tohka¹  Riccardo De Feo² 

[1] A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland;SAIMLAL Department (Human Anatomy, Histology, Forensic Medicine and Orthopedics), Sapienza Università di Roma, Rome, Italy;

关键词: brain MRI;    CNN;    deep learning;    rat;    registration;    segmentation;   

DOI  :  10.3389/fneur.2022.820267

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown