用WORD数据集做腹部多器官分割任务时，将数据集转换成相应的.npz格式和.npy.h5格式，测试数据如图，测试数据中的 mean_dice 和 mean_hd95 值太低，是由于什么原因造成的？

预测原因如下：

1.转换.npz和.npy.h5文件出错，转换代码如下

2.换数据集后dataset_synapse.py需要修改，但我没有修改？

3.在官方代码下的问答发现是否是utils.py中对dice和hd95的计算有关？

一直找不到原因在哪里，如果有大佬告诉我原因出在哪里就太好了，谢谢！！！

转换npz和npy.h5代码如下

import numpy as np
import nibabel as nib
import h5py
import os
from scipy.ndimage import zoom

# 设定路径
base_path = r"E:\Semantic Segmentation\tradition\transUnet\project_TransUNet(1)"
train_img_folder = os.path.join(base_path, r"E:\SemanticSegmentation\database\word\Training-Testing\img\imageTr")
test_img_folder = os.path.join(base_path, r"E:\SemanticSegmentation\database\word\Training-Testing\img\imageTs")
train_label_folder = os.path.join(base_path, r"E:\SemanticSegmentation\database\word\Training-Testing\label\labelTr")
test_label_folder = os.path.join(base_path, r"E:\SemanticSegmentation\database\word\Training-Testing\label\labelTs")
train_save_folder = os.path.join(base_path, r"E:\SemanticSegmentation\tradition\transUnet\project_TransUNet(1)\data\Synapse\train_npz")
test_save_folder = os.path.join(base_path, r"E:\SemanticSegmentation\tradition\transUnet\project_TransUNet(1)\data\Synapse\test_vol_h5")

# 创建保存路径
os.makedirs(train_save_folder, exist_ok=True)
os.makedirs(test_save_folder, exist_ok=True)

def filter_labels(label_data, key_labels=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]):
    """
    将标签中保留的关键标签设为原值，其余标签设为0
    """
    filtered_labels = np.zeros_like(label_data)
    for key in key_labels:
        filtered_labels[label_data == key] = key
    return filtered_labels

def contains_key_labels(slice_label, key_labels=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]):
    """
    检查切片是否包含关键标签，且背景占比不为 100%
    """
    total_pixels = slice_label.size
    background_pixels = np.sum(slice_label == 0)
    background_ratio = background_pixels / total_pixels
    contains_labels = np.isin(slice_label, key_labels).any()

    # 只保留包含关键标签且背景占比不为 100% 的切片
    return contains_labels or background_ratio < 1.0

# 修改后的测试集数据保存逻辑
for filename in os.listdir(test_img_folder):
    if filename.endswith('.nii.gz'):
        img_path = os.path.join(test_img_folder, filename)
        label_path = os.path.join(test_label_folder, filename) if os.path.exists(
            os.path.join(test_label_folder, filename)) else None

        # 加载图像和标签
        img = nib.load(img_path)
        data = img.get_fdata()
        label_data = nib.load(label_path).get_fdata() if label_path else np.zeros(data.shape)  # 如果没有标签，填充全0

        # 直接使用原始数据，无需重采样
        data_resampled = data
        label_resampled = label_data

        # 只保留标签1-8，其他标签归为0
        label_resampled = filter_labels(label_resampled)

        # 查找包含关键标签的第一个和最后一个切片索引
        start_slice = None
        end_slice = None

        for slice_idx in range(label_resampled.shape[2]):
            if contains_key_labels(label_resampled[:, :, slice_idx]):
                if start_slice is None:
                    start_slice = slice_idx
                end_slice = slice_idx  # 不断更新end_slice，直到最后一个关键标签切片

        # 如果找到关键标签的范围
        if start_slice is not None and end_slice is not None:
            # 裁剪数据到包含关键标签的切片范围
            data_clipped = data_resampled[:, :, start_slice:end_slice+1]
            label_clipped = label_resampled[:, :, start_slice:end_slice+1]

            # 数据裁剪和归一化
            data_clipped = np.clip(data_clipped, -125, 275)
            data_normalized = (data_clipped - (-125)) / (275 - (-125))

            # 保存3D体数据为 .npy.h5 文件
            num_slices = data_clipped.shape[2]
            with h5py.File(os.path.join(test_save_folder, f"case_{filename.split('.')[0]}.npy.h5"), 'w') as f:
                f.create_dataset("image", data=data_clipped)
                f.create_dataset("label", data=label_clipped)

            # 打印处理完成的信息
            print(f"文件 {filename} 中处理完成，共生成 {num_slices} 张切片。")
        else:
            print(f"文件 {filename} 中没有发现关键标签的切片。")

# 修改后的训练集数据保存逻辑
for filename in os.listdir(train_img_folder):
    if filename.endswith('.nii.gz'):
        img_path = os.path.join(train_img_folder, filename)
        label_path = os.path.join(train_label_folder, filename) if os.path.exists(
            os.path.join(train_label_folder, filename)) else None

        # 加载图像和标签
        img = nib.load(img_path)
        data = img.get_fdata()
        label_data = nib.load(label_path).get_fdata() if label_path else np.zeros(data.shape)  # 如果没有标签，填充全0

        # 直接使用原始数据，无需重采样
        data_resampled = data
        label_resampled = filter_labels(label_data)

        # 查找包含关键标签的第一个和最后一个切片索引
        start_slice = None
        end_slice = None

        for slice_idx in range(label_resampled.shape[2]):
            if contains_key_labels(label_resampled[:, :, slice_idx]):
                if start_slice is None:
                    start_slice = slice_idx
                end_slice = slice_idx  # 不断更新end_slice，直到最后一个关键标签切片

        # 如果找到关键标签的范围
        if start_slice is not None and end_slice is not None:
            # 裁剪数据到包含关键标签的切片范围
            data_clipped = data_resampled[:, :, start_slice:end_slice + 1]
            label_clipped = label_resampled[:, :, start_slice:end_slice + 1]

            # 数据裁剪和归一化
            data_clipped = np.clip(data_clipped, -125, 275)
            data_normalized = (data_clipped - (-125)) / (275 - (-125))

            # 保存每个切片为单独的 .npz 文件
            for i in range(data_normalized.shape[2]):
                slice_image = data_normalized[:, :, i]
                slice_label = label_clipped[:, :, i]
                np.savez_compressed(
                    os.path.join(train_save_folder, f"case_{filename.split('.')[0]}_slice_{i:03d}.npz"),
                    image=slice_image,
                    label=slice_label
                )

            # 打印处理完成的信息
            print(f"文件 {filename} 处理完成并保存为多个 .npz 格式。")
        else:
            print(f"文件 {filename} 中没有发现关键标签的切片。")