Magnetic resonance imaging is an essential tool for the identification of neurological problems since it provides relevant information on brain development. The aim of the present work was the detection of neurological alterations in newborns from 4 to 12 months of age by segmentation and analysis of lateral ventricles in magnetic resonance images. For this purpose, an automated deep approach based on U‐net is proposed to segment the cerebral ventricles of the newborn. Subsequently, for these regions, features were extracted based on the patient's clinical history and on the shape (area, roundness, normalized central moment, among others) and pixel intensity (mean gray value, contrast level, among others). Once the features were extracted, different types of intelligent models (Logistic Regression, k‐Nearest Neighbors (kNN), and a Convolutional Neural Network) were assessed to detect the presence of neurological alterations. The segmentation phase of the system was tested on 50 patients and the classification phase on 28 patients (11 healthy, 17 with neurological changes). The results show a DICE similarity coefficient of 0.89 and a volume ratio of 1.05 for the segmentation stage and an accuracy of 98%, precision of 100%, sensitivity of 92%, and specificity of 100% for the classification stage using kNN. The last one proved to be the most computationally feasible model, due to the time required for training and inference (0.36 s and 35.2e‐4 s, respectively), as well as the consumption of computational resources (0.1 GB RAM CPU). In conclusion, it is possible to detect neurological alterations in newborns aged 4 to 12 months by segmenting and classifying the lateral ventricles in magnetic resonance images, using image processing techniques, the U‐net, as well as the kNN algorithm. This proposed methodology could play an important role in the early diagnosis and treatment of neurological disorders.

中文翻译：

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4~12个月婴儿神经改变的MRI智能诊断

磁共振成像是识别神经系统问题的重要工具，因为它提供了大脑发育的相关信息。目前工作的目的是通过磁共振图像中侧脑室的分割和分析来检测 4 至 12 个月大的新生儿的神经学改变。为此，提出了一种基于 U-net 的自动深度方法来分割新生儿的脑室。随后，对于这些区域，根据患者的临床病史和形状（面积、圆度、标准化中心矩等）和像素强度（平均灰度值、对比度水平等）提取特征。提取特征后，将评估不同类型的智能模型（逻辑回归、k 最近邻 (kNN) 和卷积神经网络）以检测神经系统改变的存在。该系统的分割阶段在 50 名患者身上进行了测试，分类阶段在 28 名患者身上进行了测试（11 名健康患者，17 名有神经系统变化）。结果显示，分割阶段的 DICE 相似系数为 0.89，体积比为 1.05，使用 kNN 的分类阶段的准确度为 98%，精密度为 100%，灵敏度为 92%，特异性为 100%。由于训练和推理所需的时间（分别为 0.36 秒和 35.2e-4 秒）以及计算资源的消耗（0.1 GB RAM CPU），最后一个模型被证明是计算上最可行的模型。总之，通过使用图像处理技术、U-net 以及 kNN 算法对磁共振图像中的侧脑室进行分割和分类，可以检测 4 至 12 个月新生儿的神经系统改变。这种提出的方​​法可以在神经系统疾病的早期诊断和治疗中发挥重要作用。