"numTraining": 80, "numTest": 20, "training": [ {"image": "./imagesTr/patient_001_0000.nii.gz", "label": "./labelsTr/patient_001.nii.gz"}, {"image": "./imagesTr/patient_001_0001.nii.gz", "label": ""}, {"image": "./imagesTr/patient_002_0000.nii.gz", "label": "./labelsTr/patient_002.nii.gz"}, {"image": "./imagesTr/patient_002_0001.nii.gz", "label": ""} ], "test": [ "./imagesTs/test_patient_001_0000.nii.gz", "./imagesTs/test_patient_001_0001.nii.gz", "./imagesTs/test_patient_002_0000.nii.gz", "./imagesTs/test_patient_002_0001.nii.gz" ] "numTraining": 80, 是指imagesTr文件总数么，还有 {"image": "./imagesTr/patient_002_0000.nii.gz", "label": "./labelsTr/patient_002.nii.gz"}, {"image": "./imagesTr/patient_002_0001.nii.gz", "label": ""} 多模态为什么要这么写

### NNUnetv2 中 `dataset.json` 文件的关键参数解析 #### 关键参数含义 - **`numTraining` 参数**： 此参数指定了训练集中图像的数量。具体来说，它是用来告诉模型有多少张图片被标记为训练用途[^1]。这个数量应当与实际提供的训练数据相匹配，以便后续流程能够正确加载这些数据。 - **`numTest` 参数**： 类似于 `numTraining`，该参数记录的是测试集中未标注图像的总数。值得注意的是，这里的“测试”通常指的是推理阶段所用的数据集合，并不一定涉及任何性能评估指标计算过程[^2]。 #### 多模态 NIfTI 数据配置示例及原因分析 当处理多模态医学影像时（例如 MRI 扫描中的 T1 和 T2 序列），每种模式下的扫描都会形成独立的一组三维体素矩阵。因此，在构建适用于 nnU-Net v2 的输入格式之前，我们需要先理解其内部工作机制——即如何识别并关联来自同一病人的多个视角或时间点上的观测结果。 ##### 示例配置如下所示： ```json { "name": "ExampleDataset", "description": "An example of multi-modality medical imaging dataset.", "reference": "", "licence": "CC BY-SA 4.0", "release": "1.0", "tensorImageSize": "3D", /* Modality definitions */ "modality": { "0": "T1", // First modality is defined here. "1": "T2" // Second modality definition follows immediately after first one without any separator between them except comma separating key-value pairs inside object literal syntax used throughout entire file including this section too where we define two different types modalities available within our current project scope namely t1 weighted images represented by index zero followed directly afterwards another entry corresponding second type which happens be t2weighted ones indicated via string value assigned against integer key number one respectively according above mentioned convention adopted widely across many similar projects dealing specifically with multimodal datasets comprised entirely outfrom various kinds scans performed under distinct conditions but still belonging same patient group overall thus requiring special treatment during preprocessing stages prior feeding into deep learning models like those implemented through popular frameworks such Pytorch TensorFlow etcetera depending upon specific requirements set forth individual use cases accordingly.*/ }, /* Labels mapping */ "labels": { "background": 0, "organ_of_interest_1": 1, "organ_of_interest_2": 2 }, /* Number of samples in training and testing sets */ "numTraining": 80, "numTest": 20, /* Training data specification */ "training": [ {"image":"./imagesTr/case_001_modality_0000.nii.gz","label":"./labelsTr/case_001_label.nii.gz"}, {"image":"./imagesTr/case_001_modality_0001.nii.gz","label":""}, ... ], /* Testing data paths only*/ "test":[ "./imagesTs/case_test_001_modality_0000.nii.gz", "./imagesTs/case_test_001_modality_0001.nii.gz", ... ] } ``` #### 原因剖析 1. **为何采用特定命名规则？** - 每个病人实例下的所有模态均需按统一前缀加序号的方式命名，便于程序自动配对属于同一个病例的不同视图资料[^3]。 2. **为什么强调伪三维度转换？** - 即使源材料原本只是二维切片序列构成的整体结构，也需要额外增加一维使其成为符合标准接口需求的形式 `(C,H,W)` 或者更常见的情形下扩展至真正的体积表达形式 `(Z,Y,X)` ，这样才能顺利导入基于卷积神经网络设计的各种算法框架当中去执行进一步操作步骤比如特征提取分类等等任务目标达成最终效果优化提升等方面考虑因素综合作用所致的结果表现出来而已啦😊！ --- ###