Data_Sheet_1_Convolutional Neural Networks Enable Robust Automatic Segmentation of the Rat Hippocampus in MRI After Traumatic Brain Injury.PDF

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.

基于登记的方法在磁共振（MR）脑图像的自动分割中得到了广泛应用。然而，这些方法对可能改变脑部解剖结构并影响图谱图像与目标图像对齐的显著病理变化缺乏鲁棒性。在本研究中，我们开发了一种鲁棒的算法MU-Net-R，用于基于U-net-like卷积神经网络（CNNs）集成自动分割正常和受损大鼠海马体。MU-Net-R通过侧向液体冲击法对假手术大鼠和创伤性脑损伤（TBI）大鼠的手动分割MR图像进行训练。MU-Net-R的性能通过与基于单一和多个图谱登记的方法的定量比较进行了评估，这些方法使用了来自两个大型临床前队列的MR图像。MU-Net-R的自动分割质量和多图谱登记相当出色，即使在存在脑部损伤、萎缩和脑室扩大的情况下，其交叉验证的Dice分数也高于0.90。相比之下，单一图谱分割的性能不令人满意（交叉验证的Dice分数低于0.85）。有趣的是，基于登记的方法在分割对侧海马体方面优于同侧海马体，而MU-Net-R在对侧和同侧海马体的分割上表现相当。我们通过使用我们的自动分割工具来评估TBI后海马体损伤的进展。我们的数据显示，TBI的存在、TBI后的时间以及海马体是否位于损伤的同侧或对侧，是解释海马体体积的参数。