医学图像分析中的深度学习与经典计算机视觉技术：脑部MRI组织分割、肺部CT COPD配准和皮肤病变分类的案例研究
Deep learning and classical computer vision techniques in medical image analysis: Case studies on brain MRI tissue segmentation, lung CT COPD registration, and skin lesion classification
AI总结：
本研究首次系统评估医学图像分割、配准和分类任务，结合深度学习和经典方法，发现nnU-Net在脑分割最优，Elastix在肺配准最优，深度学习集成在皮肤病变分类最优，并验证了传统方法的互补价值。
摘要：
医学影像涵盖多种任务和模态，在疾病诊断、治疗规划和监测中发挥着关键作用。本研究提出了一种新颖的探索，首次系统性地评估了跨多种影像模态的分割、配准和分类任务。通过整合经典方法和深度学习方法，分别应用于脑部MRI组织分割、肺部CT图像配准以及皮肤镜图像中的皮肤病变分类，我们展示了这些方法在不同应用中的互补优势。对于脑组织分割，3D深度学习模型优于2D和基于补丁的模型，其中nnU-Net的Dice系数达到0.9397，而基于ResNet34骨干的3D U-Net模型也取得了具有竞争力的结果，Dice系数为0.8946。多图谱方法为深度学习不可行的情况提供了稳健的替代方案，平均Dice系数达到0.7267。在肺部CT配准中，经典的基于Elastix的方法优于深度学习模型，达到了6.68毫米的最小目标配准误差（TRE），突显了参数调整的有效性。HighResNet在深度学习模型中表现最佳，TRE为7.40毫米。对于皮肤病变分类，深度学习模型的集成如InceptionResNetV2和ResNet50表现出色，在二分类和多分类任务中分别达到了90.44%和93.62%的准确率。此外，采用One-vs-All方法，深度学习在黑色素瘤与其他、基底细胞癌与其他、鳞状细胞癌与其他分类中分别达到了94.64%、95.35%和96.93%的准确率，而机器学习模型，特别是基于手工特征的多层感知器（MLP），提供了可解释的替代方案，在多分类任务中使用SMOTE进行类别不平衡校正后准确率为85.04%，在二分类任务中为83.27%。源代码链接可根据要求提供。
英文摘要：
Medical imaging spans diverse tasks and modalities which play a pivotal role in disease diagnosis, treatment planning, and monitoring. This study presents a novel exploration, being the first to systematically evaluate segmentation, registration, and classification tasks across multiple imaging modalities. Integrating both classical and deep learning (DL) approaches in addressing brain MRI tissue segmentation, lung CT image registration, and skin lesion classification from dermoscopic images, we demonstrate the complementary strengths of these methodologies in diverse applications. For brain tissue segmentation, 3D DL models outperformed 2D and patch-based models, specifically nnU-Net achieving Dice of 0.9397, with 3D U-Net models on ResNet34 backbone, offering competitive results with Dice 0.8946. Multi-Atlas methods provided robust alternatives for cases where DL methods are not feasible, achieving average Dice of 0.7267. In lung CT registration, classical Elastix-based methods outperformed DL models, achieving a minimum Target Registration Error (TRE) of 6.68 mm, highlighting the effectiveness of parameter tuning. HighResNet performed best among DL models with a TRE of 7.40 mm. For skin lesion classification, ensembles of DL models like InceptionResNetV2 and ResNet50 excelled, achieving up to 90.44%, and 93.62% accuracies for binary and multiclass classification respectively. Also, adopting One-vs-All method, DL attained accuracies of 94.64% (mel vs. others), 95.35% (bcc vs. others), and 96.93% (scc vs. others), while ML models specifically Multi-Layer Perceptron (MLP) on handcrafted features offered interpretable alternatives with 85.04% accuracy using SMOTE for class imbalance correction on the multi-class task and 83.27% on the binary-class task. Links to source code are available on request.
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