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Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

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Open-source, fully-automated hybrid cardiac substructure segmentation: development and optimisation.

Robert N Finnegan^1,2,3, Vicky Chin^4,5,6, Phillip Chlap^4,5,6

¹Northern Sydney Cancer Centre, Royal North Shore Hospital, St Leonards, NSW, Australia. robert.finnegan@sydney.edu.au.

Physical and Engineering Sciences in Medicine

|February 13, 2023

Summary

This summary is machine-generated.

A new deep learning model accurately segments 18 cardiac substructures on CT scans for radiotherapy planning. This automated method improves cardiac dose evaluation and risk assessment in cancer patients.

Keywords:

Breast cancer Cardiac substructures Cardiotoxicity Deep learning Image segmentation Lung cancer Radiotherapy

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Area of Science:

Medical Imaging
Radiotherapy Physics
Computational Anatomy

Background:

Radiotherapy for thoracic and breast cancers can cause cardiotoxicity.
Cardiac substructure doses are increasingly recognized as predictive of specific outcomes.
Accurate segmentation of cardiac substructures is crucial for developing clinical planning constraints but is currently lacking.

Purpose of the Study:

To develop a novel, automated model for accurate and anatomically consistent segmentation of 18 cardiac substructures on CT scans.
To provide quantitative data for developing clinical planning constraints in radiotherapy.
To facilitate precise evaluation of cardiac doses and associated risks.

Main Methods:

A multi-stage deep learning (DL) model combined with multi-atlas mapping and geometric modeling was developed.
The model automatically segmented the whole heart, chambers, great vessels, valves, coronary arteries, and conduction nodes.
Segmentation performance was evaluated using Dice Similarity Coefficient (DSC), Mean Distance to Agreement (MDA), Hausdorff Distance (HD), and volume ratio on 30 manually contoured CT scans.

Main Results:

The automated model demonstrated reliable and accurate segmentation across various cardiac substructures, including challenging cases.
Median DSC ranged from 0.81-0.93 for heart and chambers, 0.43-0.76 for great vessels and nodes, and 0.22-0.53 for valves.
Median MDA was below 6 mm, median HD ranged from 7.7-19.7 mm, and median volume ratio was close to one for most structures.
The fully automatic algorithm processed each case in 9-23 minutes.

Conclusions:

The proposed fully-automatic method accurately delineates cardiac substructures on radiotherapy planning CT scans.
Robust and anatomically consistent segmentations, especially for smaller structures, are a significant advantage.
The open-source software will enable more precise cardiac dose and risk evaluations from clinical datasets.