This dataset contains four sample DICOM Segmentation files with the new "LABELMAP" SegmentationType that was included into the DICOM standard in the 2024c version. The DICOM Standard supplement number 243 describing this new format is found here. Briefly, this format stores one or more non-overlapping segmentas in the same pixel array where the integer value of a pixel identifies the segment that the pixel belongs to. The primary purpose of releasing these files is to make examples of these objects available as test objects for other libraries and software tools, including viewers. The providing segmentations are "re-encodings" of an existing BINARY segmentation that is publicly available in the Imaging Data Commons (IDC) repository. It contains 74 segments of various abdominal organs in an abdominal CT. The CT image series that was segmented to produce that segmentation is also available in the IDC as case "100002" from the NLST collection. The identifiers for this series are: StudyInstanceUID: 1.2.840.113654.2.55.68425808326883186792123057288612355322SeriesInstanceUID: 1.2.840.113654.2.55.229650531101716203536241646069123704792 Both the BINARY segmentation and its source image are downloaded from the IDC as part of the Python code snippet below. Alternatively you can use the idc-index tool to download e.g. the source image series: idc download 1.2.840.113654.2.55.229650531101716203536241646069123704792 You can view this in the IDC OHIF instance with the existing BINARY segmentation here. Please consult the IDC project documentation for further ways to access those files. Four variants are provided using two different values for Photometric Interpretation ("MONOCHROME2" and "PALETTE COLOR") and two different transfer syntaxes (ExplicitVRLittleEndian ("native") and JPEG2000Lossless): ct_labelmap_monochrome_native.dcm StudyInstanceUID: 1.2.840.113654.2.55.68425808326883186792123057288612355322 SeriesInstanceUID: 1.2.826.0.1.3680043.10.511.3.39163641237923761484492278922466793 SOPInstanceUID: 1.2.826.0.1.3680043.10.511.3.54215185833283589552918444094454937 Size: 32MB ct_labelmap_monochrome_jpeg2000.dcm StudyInstanceUID: 1.2.840.113654.2.55.68425808326883186792123057288612355322 SeriesInstanceUID: 1.2.826.0.1.3680043.10.511.3.78021852011276185743765835838835269 SOPInstanceUID: 1.2.826.0.1.3680043.10.511.3.54215185833283589552918444094454937 Size: 1.7MB ct_labelmap_palette_color_native.dcm StudyInstanceUID: 1.2.840.113654.2.55.68425808326883186792123057288612355322 SeriesInstanceUID: 1.2.826.0.1.3680043.10.511.3.1928618149102457371215393385865002 SOPInstanceUID: 1.2.826.0.1.3680043.10.511.3.49665993503894378485170565809581412 Size: 32MB ct_labelmap_palette_color_jpeg2000.dcm StudyInstanceUID: 1.2.840.113654.2.55.68425808326883186792123057288612355322 SeriesInstanceUID: 1.2.826.0.1.3680043.10.511.3.60058092962333326814689785333007126 SOPInstanceUID: 1.2.826.0.1.3680043.10.511.3.35953210903734988696114857448634090 Size: 1.8MB Anyone can produce these example files (although the UIDs will change) using the highdicom Python library (version 0.25.0 or higher) and the following Python code: from copy import deepcopy from matplotlib import colormaps import numpy as np from pathlib import Path import highdicom as hd from pydicom.sr.codedict import codes from idc_index import IDCClient from pydicom.uid import JPEG2000Lossless, ExplicitVRLittleEndian # Need this guard because of possibility of threading (when using workers) if __name__ == '__main__': # The series instance UID of an existing IDC segmentation (created using # TotalSegmentator on the NLST collection). We will download this and # re-encode it in a new LABELMAP segmentation segmentation_series_uid = "1.2.276.0.7230010.3.1.3.313263360.31993.1706319455.429793" # Temporary download directory for downloads and new files download_dir = Path('total_segmentator_downloads') download_dir.mkdir(exist_ok=True) # Download the original (binary) segmentation and load it client = IDCClient() client.download_dicom_series( segmentation_series_uid, downloadDir=download_dir, dirTemplate='%SeriesInstanceUID' ) seg_file = list((download_dir / segmentation_series_uid).glob('*.dcm'))[0] seg = hd.seg.segread(seg_file) # Download the files of the segmented CT series and load them segmented_series_uid = seg.get_source_image_uids()[0][1] client.download_dicom_series( segmented_series_uid, downloadDir=download_dir, dirTemplate='%SeriesInstanceUID' ) ct_files = list((download_dir / segmented_series_uid).glob('*.dcm')) images = [hd.imread(p) for p in ct_files] # Sort the image datasets so that they match the eventual segmentation # volume images = hd.spatial.sort_datasets(images) # Get the original segmentation pixel array as a volume, and combine into # "labelmap" form at this point seg_vol = seg.get_volume(combine_segments=True) # The original segmentation is rotated relative to the source images, for # whatever reason. So we either have to a) pass the segmentation pixel # array to the Segmentation constructor as a Volume object, or b) do the # spatial operations necessary to align the segmentation pixel array to the # source images. Both will make the spatial metadata correct in the new # segmentation, but if we do option a), highdicom will not establish # per-frame correspondences in the PerFrameFunctionalGroupsSequence. So we # opt for option b) using the match_geometry method im_vol = hd.get_volume_from_series(images) seg_vol = seg_vol.match_geometry(im_vol) # Describe the algorithm that created the segmentation (though this is not # strictly required in the standard and not present in the original # segmentation, highdicom requires it) algorithm_identification = hd.AlgorithmIdentificationSequence( name='TotalSegmentator', version='v1.5.6', family=codes.cid7162.ArtificialIntelligence ) orig_segment_descriptions = [ seg.get_segment_description(i) for i in seg.segment_numbers ] for desc in orig_segment_descriptions: desc.SegmentationAlgorithmIdentificationSequence = algorithm_identification # Create an 8-bit palette color LUT using matplotlib's' built-in # 'gist_rainbow_r' colormap cmap = colormaps['gist_rainbow_r'] num_entries = seg.number_of_segments # e.g. number of classes in a segmentation lut_data = cmap(np.arange(num_entries) / (num_entries + 1), bytes=True) lut_data = np.vstack([np.zeros((1, 4), dtype=lut_data.dtype), lut_data]) lut = hd.PaletteColorLUTTransformation.from_combined_lut( lut_data=lut_data[:, :3], # remove alpha channel palette_color_lut_uid=hd.UID(), ) i = 1 for compress in [False, True]: for palette_color in [False, True]: if palette_color: # If using palette color, the recommended cielab values are disallowed segment_descriptions = deepcopy(orig_segment_descriptions) for desc in segment_descriptions: del desc.RecommendedDisplayCIELabValue else: segment_descriptions = orig_segment_descriptions transfer_syntax_uid = ( JPEG2000Lossless if compress else ExplicitVRLittleEndian ) pi_str = 'Palette Color' if palette_color else 'Monochrome' syntax_str = 'JPEG2000' if compress else 'Native' series_description = ( f"TotalSegmentator(v.1.5.6) Labelmap Seg, {pi_str} {syntax_str}" ) labelmap_seg = hd.seg.Segmentation( source_images=images, pixel_array=seg_vol.array, # move to array otherwise spatial correspondences are not preserved segment_descriptions=segment_descriptions, segmentation_type="LABELMAP", manufacturer=seg.Manufacturer, manufacturer_model_name=seg.ManufacturerModelName, software_versions=seg.SoftwareVersions, device_serial_number=seg.DeviceSerialNumber, series_instance_uid=hd.UID(), sop_instance_uid=hd.UID(), series_number=seg.SeriesNumber + i, instance_number=1, workers=12 if compress else 0, palette_color_lut_transformation=lut if palette_color else None, transfer_syntax_uid=transfer_syntax_uid, series_description=series_description, ) fname = ( download_dir / f'ct_labelmap_{pi_str}_{syntax_str}.dcm'.lower().replace(' ', '_') ) labelmap_seg.save_as(fname) i += 1 This data was encoded by the NCI Imaging Data Commons project, which has been funded in whole or in part with Federal funds from the NCI, NIH, under task order no. HHSN26110071 under contract no. HHSN261201500003l.