Lesta Gloria D, Deserno Thomas M, Abani Samira, Janisch Jörg, Hänsch Alexej, Laue Merlin, Winzer Stefanie, Dickinson Peter J, De Decker Steven, Gutierrez-Quintana Rodrigo, Subbotin Aleksandr, Bocharova Kseniia, McLarty Ehren, Lemke Laura, Wang-Leandro Adriano, Spohn Franziska, Volk Holger A, Nessler Jasmin N
DOS Software-Systeme GmbH, Wolfsburg, Germany.
Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Braunschweig, Germany.
BMC Vet Res. 2025 Sep 16;21(1):537. doi: 10.1186/s12917-025-05003-4.
Brain extraction is a common preprocessing step when working with intracranial medical imaging data. While several tools exist to automate the preprocessing of magnetic resonance imaging (MRI) of the human brain, none are available for canine MRIs. We present a pipeline mapping separate 2D scans to a 3D image, and a neural network for canine brain extraction.
The training dataset consisted of T1-weighted and contrast-enhanced images from 68 dogs of different breeds, all cranial conformations (mesaticephalic, dolichocephalic, brachycephalic), with several pathological conditions, taken at three institutions. Testing was performed on a similarly diverse group of 10 dogs with images from a 4th institution.
The model achieved excellent results in terms of Dice ([Formula: see text]) and Jaccard ([Formula: see text]) metrics and generalised well across different MRI scanners, the three aforementioned skull types, and variations in head size and breed. The pipeline was effective for a combination of one to three acquisition planes (i.e., transversal, dorsal, and sagittal). Aside from the T1 weighted imaging training datasets, the model also performed well on other MRI sequences with Jaccardian indices and median Dice scores ranging from 0.86 to 0.89 and 0.92 to 0.94, respectively.
Our approach was robust for automated brain extraction. Variations in canine anatomy and performance degradation in multi-scanner data can largely be mitigated through normalisation and augmentation techniques. Brain extraction, as a preprocessing step, can improve the accuracy of an algorithm for abnormality classification in MRI image slices.
在处理颅内医学影像数据时，脑提取是一个常见的预处理步骤。虽然有多种工具可用于自动对人类大脑的磁共振成像（MRI）进行预处理，但尚无适用于犬类MRI的工具。我们提出了一种将单独的二维扫描映射到三维图像的流程，以及一个用于犬脑提取的神经网络。
训练数据集由来自三个机构的68只不同品种、具有各种头颅形态（中脑型、长头型、短头型）且患有多种病理状况的犬的T1加权图像和增强对比图像组成。在由来自第四家机构的图像组成的另一组同样多样化的10只犬上进行测试。
该模型在Dice（[公式：见原文]）和Jaccard（[公式：见原文]）指标方面取得了优异的结果，并且在不同的MRI扫描仪、上述三种颅骨类型以及头部大小和品种的变化中都具有良好的泛化能力。该流程对于一到三个采集平面（即横向、背侧和矢状面）的组合有效。除了T1加权成像训练数据集外，该模型在其他MRI序列上也表现良好，Jaccard指数和中位数Dice分数分别在0.86至0.89和0.92至0.94之间。
我们的方法对于自动脑提取具有鲁棒性。通过归一化和增强技术，可以在很大程度上减轻犬类解剖结构的变化以及多扫描仪数据中性能的下降。作为预处理步骤的脑提取可以提高MRI图像切片中异常分类算法的准确性。
