对比深度学习与传统方法在医学图像分析中的表现，覆盖脑部、肺部和皮肤三类任务。
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
- 融合深度学习与经典算法，分任务评估不同方法的优劣
- 3D神经网络在脑组织分割中达0.9397的Dice系数，优于2D模型
- 传统方法在肺部配准中更优，且可解释性强，适合资源受限场景
医学影像涵盖多种任务与模态，在疾病诊断、治疗规划与监测中起关键作用。本研究首次系统性地评估了跨多模态的分割、配准与分类任务，结合深度学习（DL）与经典计算机视觉方法，应用于脑部MRI组织分割、肺部CT慢性阻塞性肺病（COPD）配准及皮肤病变分类。在脑组织分割中，3D DL模型表现最优，nnU-Net达到0.9397的Dice系数，3D U-Net（ResNet34骨干）达0.8946；多图谱法在无法使用DL时提供稳定替代，平均Dice为0.7267。肺部配准中，基于Elastix的经典方法优于DL模型，最低目标配准误差（TRE）为6.68 mm；HighResNet为最佳DL模型，TRE为7.40 mm。皮肤病变分类中，集成DL模型如InceptionResNetV2与ResNet50分别实现二分类90.44%、多分类93.62%准确率；采用One-vs-All策略，对良恶性、基底细胞癌、鳞状细胞癌分类准确率分别达94.64%、95.35%、96.93%；而基于手工特征的MLP模型经SMOTE处理后，多分类准确率达85.04%，二分类为83.27%。代码可应要求提供。
原文摘要 · Abstract (English)
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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