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+# 1. Goal: 使用 YoloV8 对医学影像CT/磁共振图像进行肺炎区域分割:
+
+### part1 :
+A. init data [nii(Nifit) -> png]: 将CT原始数据从 三维转二维, 保存png格式
+B. mask data [nii -> json]: 将CT的分割标注数据从 三维转二维, 再转Labelme可以识别的json文件
+
+### part2:
+**Algorithm: Yolo-V8**
+A. make dataset [train/val/test + label]: 对二维数据和json文件进行数据划分, 并json转换Yolo标签格式
+B. Segmentation [Yolo-V8]: 使用YoloV8进行分割
+
+
+
+# 2. JSON format (eg:) :
+{
+  "version": "4.5.6",  // Labelme software version
+  "flags": {},         // Additional flag information
+  "shapes": [
+    {
+      "label": "dog",  // Label name
+      "points": [      // Polygon vertex points of the annotation 
+        [298, 151],
+        [324, 151],
+        [324, 160],
+        [317, 160],
+        [317, 168],
+        [307, 168],
+        [307, 151],
+        [298, 151]
+      ],
+      "group_id": null,
+      "shape_type": "polygon",  // Shape type
+      "flags": {}
+    },
+    {
+      "label": "cat",
+      "points": [
+        [400, 200],
+        [425, 200],
+        [425, 210],
+        [415, 210],
+        [415, 220],
+        [405, 220],
+        [405, 200],
+        [400, 200]
+      ],
+      "group_id": null,
+      "shape_type": "polygon",
+      "flags": {}
+    }
+    ...
+  ],
+  "imagePath": "path/to/image.jpg",  // Image file path
+  "imageData": null,                 // Image data (Base64 encoded)
+  "imageHeight": 480,                // Image height
+  "imageWidth": 640                  // Image width
+}
+
+
+
+
+
+YOLO .txt format:
+one object -> one row
+(classID, x_center, y_center, width_normalized, height_normalized)
+
+(eg):
+1 0.375000 0.625000 0.312500 0.416667
+0 0.656250 0.291667 0.156250 0.208333
+
+
+
+
+
+dataset.yaml format(eg):
+train: /path/to/your/dataset/images/train
+val: /path/to/your/dataset/images/val
+nc: 5
+names: ['cat', 'dog', 'person', 'car', 'truck']
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