Efficient Cerebral Infarction Segmentation Using U-Net and U-Net3 + Models.

Esra Yuce, Muhammet Emin Sahin, Hasan Ulutas, Mustafa Fatih Erkoç
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Abstract

Cerebral infarction remains a leading cause of mortality and long-term disability globally, underscoring the critical importance of early diagnosis and timely intervention to enhance patient outcomes. This study introduces a novel approach to cerebral infarction segmentation using a novel dataset comprising MRI scans of 110 patients, retrospectively collected from Yozgat Bozok University Research Hospital. Two convolutional neural network architectures, the basic U-Net and the advanced U-Net3 + , are employed to segment infarction regions with high precision. Ground-truth annotations are generated under the supervision of an experienced radiologist, and data augmentation techniques are applied to address dataset limitations, resulting in 6732 balanced images for training, validation, and testing. Performance evaluation is conducted using metrics such as the dice score, Intersection over Union (IoU), pixel accuracy, and specificity. The basic U-Net achieved superior performance with a dice score of 0.8947, a mean IoU of 0.8798, a pixel accuracy of 0.9963, and a specificity of 0.9984, outperforming U-Net3 + despite its simpler architecture. U-Net3 + , with its complex structure and advanced features, delivered competitive results, highlighting the potential trade-off between model complexity and performance in medical imaging tasks. This study underscores the significance of leveraging deep learning for precise and efficient segmentation of cerebral infarction. The results demonstrate the capability of CNN-based architectures to support medical decision-making, offering a promising pathway for advancing stroke diagnosis and treatment planning.

基于U-Net和U-Net3 +模型的脑梗死有效分割
脑梗死仍然是全球死亡和长期残疾的主要原因,这强调了早期诊断和及时干预对提高患者预后的至关重要性。本研究引入了一种新的脑梗死分割方法,该方法使用了一个新的数据集,该数据集包括从Yozgat Bozok大学研究医院回顾性收集的110例患者的MRI扫描。采用基本的U-Net和高级的U-Net3 +两种卷积神经网络架构对梗死区域进行高精度分割。在经验丰富的放射科医生的监督下生成基础真相注释,并应用数据增强技术来解决数据集限制,从而产生6732张用于训练、验证和测试的平衡图像。性能评估是使用诸如骰子分数、交集比(IoU)、像素精度和特异性等指标进行的。基本版U-Net的性能优于U-Net3 +,其dice得分为0.8947,平均IoU为0.8798,像素精度为0.9963,特异性为0.9984,尽管其架构更简单,但性能优于U-Net3 +。U-Net3 +凭借其复杂的结构和先进的功能,提供了具有竞争力的结果,突出了在医学成像任务中模型复杂性和性能之间的潜在权衡。这项研究强调了利用深度学习对脑梗死进行精确和有效分割的重要性。结果证明了基于cnn的架构支持医疗决策的能力,为推进中风诊断和治疗计划提供了一条有希望的途径。
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