通过磁共振成像（MRI）准确描绘海马区域对于神经系统疾病的预防和早期诊断至关重要。确定如何根据 MRI 结果准确、快速地描绘海马体已成为一个严重的问题。本研究提出了一种利用3D-UNet的像素级语义分割方法，实现从MRI结果中对大脑海马体的自动分割。200个三维T1加权（3D-T1）非钆对比增强磁共振（ MR）图像于2020年6月至2022年12月在杭州市肿瘤医院采集。这些样本分为两组，分别包含175个和25个样本。第一组中，145例用于训练海马分割模型，其余30例用于微调模型的超参数。第二组 25 名患者的图像被用作测试集来评估模型的性能。通过旋转、缩放、灰度值增强和图像数据和地面实况标签的平滑致密变形场的变换来处理训练图像集。将填充技术引入分割网络来建立海马分割模型。此外，将利用原始网络建立的模型（如VNet、SegResNet、UNetR和3D-UNet）与填充技术与原始分割网络相结合构建的模型进行了性能比较。结果表明，引入填充技术后，分割模型得到了改进。具体来说，当将填充技术引入VNet、SegResNet、3D-UNet和UNetR时，输入图像尺寸为48 × 48 × 48训练的模型的分割性能得到改善。其中，采用填充技术的基于3D-UNet的模型取得了最好的性能，其Dice分数（Dice Score）为0.7989 ± 0.0398，平均交集（mIoU）为0.6669 ± 0.0540，均大于其他模型。原始基于 3D-UNet 的模型。此外，过分割率（OSR）、平均表面距离（ASD）和豪斯多夫距离（HD）分别为0.0666±0.0351、0.5733±0.1018和5.1235±1.4397，优于其他模型。此外，当输入图像尺寸设置为48 × 48 × 48、64 × 64 × 64和96 × 96 × 96时，模型性能逐渐提升，模型的Dice分数达到0.7989 ± 0.0398，分别为 0.8371±0.0254 和 0.8674±0.0257。此外，mIoU分别达到0.6669 ± 0.0540、0.7207 ± 0.0370和0.7668 ± 0.0392。通过将填充技术引入分割网络构建的海马分割模型比单独在原始网络上构建的模型表现更好，并且可以提高效率诊断分析。© 2024。作者。

Accurate delineation of the hippocampal region via magnetic resonance imaging (MRI) is crucial for the prevention and early diagnosis of neurosystemic diseases. Determining how to accurately and quickly delineate the hippocampus from MRI results has become a serious issue. In this study, a pixel-level semantic segmentation method using 3D-UNet is proposed to realize the automatic segmentation of the brain hippocampus from MRI results.Two hundred three-dimensional T1-weighted (3D-T1) nongadolinium contrast-enhanced magnetic resonance (MR) images were acquired at Hangzhou Cancer Hospital from June 2020 to December 2022. These samples were divided into two groups, containing 175 and 25 samples. In the first group, 145 cases were used to train the hippocampus segmentation model, and the remaining 30 cases were used to fine-tune the hyperparameters of the model. Images for twenty-five patients in the second group were used as the test set to evaluate the performance of the model. The training set of images was processed via rotation, scaling, grey value augmentation and transformation with a smooth dense deformation field for both image data and ground truth labels. A filling technique was introduced into the segmentation network to establish the hippocampus segmentation model. In addition, the performance of models established with the original network, such as VNet, SegResNet, UNetR and 3D-UNet, was compared with that of models constructed by combining the filling technique with the original segmentation network.The results showed that the performance of the segmentation model improved after the filling technique was introduced. Specifically, when the filling technique was introduced into VNet, SegResNet, 3D-UNet and UNetR, the segmentation performance of the models trained with an input image size of 48 × 48 × 48 improved. Among them, the 3D-UNet-based model with the filling technique achieved the best performance, with a Dice score (Dice score) of 0.7989 ± 0.0398 and a mean intersection over union (mIoU) of 0.6669 ± 0.0540, which were greater than those of the original 3D-UNet-based model. In addition, the oversegmentation ratio (OSR), average surface distance (ASD) and Hausdorff distance (HD) were 0.0666 ± 0.0351, 0.5733 ± 0.1018 and 5.1235 ± 1.4397, respectively, which were better than those of the other models. In addition, when the size of the input image was set to 48 × 48 × 48, 64 × 64 × 64 and 96 × 96 × 96, the model performance gradually improved, and the Dice scores of the proposed model reached 0.7989 ± 0.0398, 0.8371 ± 0.0254 and 0.8674 ± 0.0257, respectively. In addition, the mIoUs reached 0.6669 ± 0.0540, 0.7207 ± 0.0370 and 0.7668 ± 0.0392, respectively.The proposed hippocampus segmentation model constructed by introducing the filling technique into a segmentation network performed better than models built solely on the original network and can improve the efficiency of diagnostic analysis.© 2024. The Author(s).