使用 itkimage2segimage 将 NIfTI 转换为 DICOM SEG 时出现重复帧

Question

我正在使用

dcmqi

中的 itkimage2segimage 工具将具有多个分段（例如肝脏和脾脏）的 NIfTI 文件转换为 DICOM SEG 文件。

例如：

• 输入 NIfTI： 203 个切片，带有 2 个标签（肝脏、脾脏）。
• 输出 DICOM SEG： 406 个切片（每个标签 203 个切片）。

我如何修改代码来生成组合分割或者你可以说对所有标签进行多层分割？

这是我用来执行转换的Python代码：

import nibabel as nib  # To validate NIfTI labels
import subprocess
import os
import pydicom
import json


def get_unique_labels_from_nifti(nifti_file):
    """
    Extracts unique labels from the NIfTI file.
    Args:
        nifti_file (str): Path to the NIfTI file.
    Returns:
        list: Sorted unique labels (intensity values) in the NIfTI file.
    """
    nifti_data = nib.load(nifti_file).get_fdata()
    unique_labels = sorted(set(nifti_data.flatten()))
    return [int(label) for label in unique_labels if label > 0]  # Exclude background (0)


def convert_nifti_to_dcm_seg(nifti_file, dicom_dir, output_seg_file):
    """
    Converts a NIfTI segmentation file to DICOM SEG using itkimage2segimage.

    Args:
        nifti_file (str): Path to the input NIfTI file.
        dicom_dir (str): Directory containing the DICOM files.
        output_seg_file (str): Path to save the output DICOM SEG file.
    """
    # Ensure output directory exists
    os.makedirs(os.path.dirname(output_seg_file), exist_ok=True)

    # Extract the StudyInstanceUID from one of the DICOM files in the directory
    dicom_file = os.path.join(dicom_dir, os.listdir(dicom_dir)[0])
    study_instance_uid = pydicom.dcmread(dicom_file).StudyInstanceUID

    # Validate the labels in the NIfTI file
    labels_in_nifti = get_unique_labels_from_nifti(nifti_file)
    print(f"Unique labels in NIfTI file: {labels_in_nifti}")

    # Generate segment attributes for each class
    segment_attributes = []

    # Example descriptions for each label
    descriptions = {
        labels_in_nifti[0]: "Liver",
        labels_in_nifti[1]: "Spleen"
    }

    for i, label in enumerate(labels_in_nifti):
        segment_attributes.append({
            "SegmentNumber": i+1,
            "labelID": label,
            "SegmentDescription": descriptions.get(label, "Unknown"),
            "SegmentAlgorithmType": "AUTOMATIC",
            "SegmentAlgorithmName": "TotalSegmentator",
            "SegmentedPropertyCategoryCodeSequence": {
                "CodeValue": "T-D0050" if label == labels_in_nifti[0] else "T-62000",
                "CodingSchemeDesignator": "SCT",
                "CodeMeaning": "Anatomical Structure"
            },
            "SegmentedPropertyTypeCodeSequence": {
                "CodeValue": "10200004" if label == labels_in_nifti[0] else "10200005",
                "CodingSchemeDesignator": "SCT",
                "CodeMeaning": descriptions.get(label, "Unknown")
            }
        })

    # Construct metadata
    metadata = {
        "ContentCreatorName": "Reader1",
        "ClinicalTrialSeriesID": "Session1",
        "ClinicalTrialTimePointID": "1",
        "SeriesDescription": "Segmentation",
        "SeriesNumber": "300",
        "InstanceNumber": "1",
        "BodyPartExamined": "Liver and Spleen",
        "segmentAttributes": [segment_attributes],
        "ContentLabel": "SEGMENTATION",
        "ContentDescription": "Image segmentation",
        "ClinicalTrialCoordinatingCenterName": "dcmqi"
    }

    # Save metadata to a JSON file
    metadata_file = os.path.join(os.path.dirname(output_seg_file), "metadata.json")
    with open(metadata_file, "w") as f:
        json.dump(metadata, f, indent=4)

    # Construct the command to convert the NIfTI to DICOM SEG
    cmd = [
        r'/media/zain/New Volume/PycharmProjects/XRAD/.venv/lib/python3.10/site-packages/dcmqi/bin/itkimage2segimage',
        "--inputImageList", nifti_file,
        "--inputMetadata", metadata_file,
        "--outputDICOM", output_seg_file,
        "--inputDICOMDirectory", dicom_dir,
        "--skip", "0"
    ]

    try:
        # Execute the command
        result = subprocess.run(cmd, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
        print("Conversion successful!")
        print("Output:", result.stdout)
    except subprocess.CalledProcessError as e:
        # Handle errors
        print("Error during conversion:")
        print("Output:", e.stdout)
        print("Error:", e.stderr)


# Example usage
nifti_file = "niftis/2.25.1937671288494678696440169598763456789/2.25.1937671288494678696440169598763456789_seg.nii.gz"  # Path to your NIfTI file
dicom_dir = "dicoms/2.25.1937671288494678696440169598763456789/"  # Directory containing the DICOM files
output_seg_file = "./output/multi_class_seg.dcm"  # Path to save the DICOM SEG file

convert_nifti_to_dcm_seg(nifti_file, dicom_dir, output_seg_file)

上述Python脚本中使用的转换命令

itkimage2segimage --inputImageList <nifti_file> \
                  --inputMetadata <metadata.json> \
                  --outputDICOM <output_seg_file> \
                  --inputDICOMDirectory <dicom_dir> \
                  --skip 0

为了帮助调试此问题，我正在共享文件 - 代码文件

Answer 1

为什么不把脾脏标签替换为肝脏标签，然后使用现有的代码呢？类似于

nifti_data[where(nifti_data == labels_in_nifti[1])] = labels_in_nifti[0]

。