DICOM SEG for CMR cine data (2D + t)

Trying to generate a dicom seg file based on a multiple unsigned np.array, which is of shape (#_frames, #rows, #cols). The way I’ve approached the task was by generating dicom seg with the same series uid across all frames of the segmentation array. However, when displaying the output the 3 different delineated structures do not appear on a single frame (spatially they do not overlap).

Bellow is an example of the code that I’ve used to generate the dicom outputs.

Is it possible to show all the 3 structures in a single frame?

# img_dsets (list): list of pydicom.Datasets with lenght equal to the number of short-axis cine data
# seg_vol (np.array): Segmentation volume array with shape (#_frames, #rows, #cols)
import pydicom as pyd
import highdicom as hd

manufacturer_name = "dummy_manufacturer_name"
manufacturer_model_name = "dummy_model_name"
software_version = "0.1"


seg_labels = {
    "myocardium": {"class_number": 1, "sct_code": pyd.sr.codedict.codes.SCT.LeftVentricleMyocardium},
    "left_ventricle": {"class_number": 3, "sct_code": pyd.sr.codedict.codes.SCT.LeftVentricle},
    "right_ventricle": {"class_number": 2, "sct_code": pyd.sr.codedict.codes.SCT.RightVentricle},
}


algo_details = hd.AlgorithmIdentificationSequence(
    name=manufacturer_model_name,
    version=software_version,
    family=pyd.sr.codedict.codes.cid7162.ArtificialIntelligence,
    source=manufacturer_name,
)

segment_descriptions = []
for label_name, label_info in seg_labels.items():

    seg_details = hd.seg.SegmentDescription(
        segment_number=label_info["class_number"],
        segment_label=label_name,
        segmented_property_category=pyd.sr.codedict.codes.cid7150.AnatomicalStructure,
        segmented_property_type=label_info["sct_code"],
        algorithm_type=hd.seg.SegmentAlgorithmTypeValues.AUTOMATIC,
        algorithm_identification=algo_details,
        tracking_uid=hd.UID(),
        tracking_id=f"Cardiac structure #{class_number}",
    )

    segment_descriptions.append(seg_details)


series_uid = hd.UID()

for frame_idx in range(seg_vol.shape[0]):

    seg_dset = hd.seg.Segmentation(
        source_images=[img_dsets[frame_idx]],
        pixel_array=seg_vol[frame_idx],
        segmentation_type=hd.seg.enum.SegmentationTypeValues.BINARY,
        segment_descriptions=segment_descriptions,
        series_description="Segmentation-Test",
        series_number=5,
        series_instance_uid=series_uid,
        sop_instance_uid=hd.UID(),
        instance_number=int(frame_idx) + 1,
        manufacturer=manufacturer,
        manufacturer_model_name=manufacturer_model_name,
        software_versions=software_version,
        device_serial_number=str(img_dsets[frame_idx].DeviceSerialNumber),
    )

    seg_dsets.append(seg_dset)

Also, I’ve tried to generate a single dataset file for all frames at once, and with that approach the output data only shows a total number of frames equal to the # of classes, not iterating over all the frames.

[apint0-media]

I am not sure I understand your question correctly. By “structures” do you mean “segments”?

The highdicom.seg.Segmentation class provides methods for accessing the pixel data, e.g., get_pixels_by_dimension_index_values(). These methods provide a combine_segments parameter. When the argument is True, the methods will combine multiple segments into a single pixel array to form a label mask.

Thanks for the help! Think an image might be better to describe what was I referring to. The goal was to obtain something like figure bellow, for each frame (viewer: 3D Slicer)

Currently, with the above code, the output does not show the 3 regions in the same plane and does not contain all the information of the patient along the frames dimension.

I will explore the get_pixels_by_dimension_index_values(), to verify if the label map is generated accordingly.

[apint0-media]

Also, I’ve tried to use get_pixels_by_dimension_index_values but I’m having some troubles with the argument dimension_index_values. Dont’ know how to define it properly.