Simpleware Heart Segmenter

SegmentHeartSimpleware runs Synopsys Simpleware Medical’s ASCardio module as an external process and returns the resulting heart and major-vessel masks as ITK images.

Class Reference

class physiomotion4d.SegmentHeartSimpleware(log_level=20)[source]

Bases: SegmentAnatomyBase

Heart CT segmentation using Simpleware Medical’s ASCardio module.

This class implements heart segmentation using Synopsys Simpleware Medical, a commercial medical image processing platform. It specifically leverages the ASCardio module for automated cardiac segmentation. The class handles the external process communication with Simpleware Medical and converts between ITK and Simpleware image formats.

Simpleware Medical provides high-quality segmentation for cardiac structures including chambers, myocardium, and major vessels. The segmentation is performed by launching Simpleware Medical as an external process and running a Python script within the Simpleware environment.

The class maintains specific ID mappings for: - Heart structures (left/right atrium, left/right ventricle, myocardium) - Major vessels (aorta, pulmonary artery)

target_spacing

Target spacing set to 1.0mm for Simpleware.

Type:

float

simpleware_exe_path

Path to Simpleware Medical executable.

Type:

str

simpleware_script_path

Path to Simpleware Python script.

Type:

str

The heart and major-vessel labels populated by this class are accessed through the inherited SegmentAnatomyBase.taxonomy (taxonomy.labels_in_group("heart") etc.).

Example

>>> segmenter = SegmentHeartSimpleware()
>>> result = segmenter.segment(ct_image, contrast_enhanced_study=True)
>>> labelmap = result['labelmap']
>>> heart_mask = result['heart']
__init__(log_level=20)[source]

Initialize the Simpleware Medical based heart segmentation.

Sets up the Simpleware-specific anatomical structure ID mappings and processing parameters. The target spacing is set to 1.0mm which is optimal for cardiac segmentation in Simpleware Medical.

Parameters:

log_level (int | str) – Logging level (default: logging.INFO)

set_trim_branches(trim_branches)[source]

Enable trimming of pulmonary and great-vessel branches.

When enabled, trim_branches() is applied to the labelmap after Simpleware segmentation, clipping the pulmonary veins and major great vessels back to the cardiac region. Trimming reduces inter-subject variability in vessel extent, which simplifies AI-Ready and Sim-Ready model fitting. It is consistent with how vessels were trimmed in the example KCL Heart dataset.

Parameters:

trim_branches (bool) – Whether to trim branches to the cardiac region.

Return type:

None

set_simpleware_executable_path(path)[source]

Set the path to the Simpleware Medical console executable.

Parameters:

path (str) – Full path to ConsoleSimplewareMedical.exe

Return type:

None

Example

>>> segmenter.set_simpleware_executable_path(
...     "C:/Program Files/Synopsys/Simpleware Medical/X-2025.06/ConsoleSimplewareMedical.exe"
... )
segmentation_method(preprocessed_image)[source]

Run Simpleware Medical ASCardio segmentation on the preprocessed image.

This implementation calls Simpleware Medical as an external process, passing the preprocessed image via a temporary file. The Simpleware Python script (SimplewareScript_heart_segmentation.py) runs within the Simpleware environment and uses the ASCardio module for heart segmentation. The results are written as per-structure MHD mask files and assembled into a labelmap, then read back as an ITK image.

Parameters:

preprocessed_image (itk.image) – The preprocessed CT image with isotropic spacing and appropriate intensity scaling

Returns:

The segmentation labelmap with heart and vessel labels

from the ASCardio module

Return type:

itk.image

Raises:

Note

Requires a valid installation of Synopsys Simpleware Medical with the ASCardio module. The method creates temporary files for input/output communication with Simpleware.

Example

>>> labelmap = segmenter.segmentation_method(preprocessed_ct)
get_landmarks()[source]

Get the landmarks.

Return type:

dict[str, tuple[float, float, float]]

trim_branches(labelmap_image)[source]

Trim pulmonary and great-vessel branches back to the cardiac region.

Clips pulmonary veins and the great vessels (aorta, pulmonary artery) to the portions adjacent to the heart and keeps only the largest connected component of the left and right atria. Reduces inter-subject variability in vessel extent, simplifying AI-Ready and Sim-Ready model fitting. Consistent with how vessels were trimmed in the example KCL Heart dataset.

Return type:

image

Basic Usage

import itk

from physiomotion4d import SegmentHeartSimpleware

image = itk.imread("chest_ct.nrrd")
segmenter = SegmentHeartSimpleware()
masks = segmenter.segment(image, contrast_enhanced_study=True)

heart = masks["heart"]
vessels = masks["major_vessels"]

Returned Keys

The ASCardio module segments cardiac anatomy only, so this segmenter’s taxonomy registers a subset of the groups produced by SegmentChestTotalSegmentator. The returned dictionary contains:

  • labelmap

  • heart

  • major_vessels

  • soft_tissue (base-class placeholder for label id 133)

  • contrast (base-class placeholder for label id 135)

  • other (all unclaimed label ids in [1, 256))

Keys such as lung and bone are not present. Callers that need those groups must either use a different segmenter or check membership ("lung" in masks) and handle the absence explicitly. See Segmentation Base Class for the full taxonomy contract.

Installation Note

SegmentHeartSimpleware requires a licensed installation of Synopsys Simpleware Medical and its ASCardio module. The class invokes the Simpleware executable as a subprocess; the executable and helper script paths must be configured on the host system.

See Also