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Domain knowledge based comprehensive segmentation of Type-A aortic dissection with clinically-oriented evaluation.

Shanshan Song1, Hailong Qiu2, Meiping Huang3

  • 1Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong.

Medical Image Analysis
|March 6, 2025
PubMed
Summary
This summary is machine-generated.

Type-A aortic dissection (TAAD) requires rapid diagnosis. We introduce imageTAAD, a dataset and SegTAAD method for automatic segmentation, improving clinical evaluation for better patient outcomes.

Keywords:
BenchmarkClinically-oriented EvaluationDomain KnowledgeMedical Image SegmentationType-A Aortic Dissection

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Area of Science:

  • Cardiovascular Imaging
  • Medical Image Analysis
  • Computational Pathology

Background:

  • Type-A aortic dissection (TAAD) is a critical cardiac emergency demanding swift diagnosis, prognosis prediction, and surgical planning for patient survival.
  • Manual segmentation of TAAD-related anatomic structures is challenging and not routinely performed in clinical practice due to disease urgency and expertise requirements.
  • Existing automatic segmentation methods for TAAD are limited by the absence of comprehensive public datasets and clinically-oriented evaluation metrics.

Purpose of the Study:

  • To address the limitations of current TAAD research by introducing imageTAAD, the first comprehensive segmentation dataset with clinically-oriented evaluation.
  • To develop and validate SegTAAD, a baseline method for comprehensive TAAD segmentation that incorporates domain knowledge.
  • To provide a publicly available dataset, code, and trained models to advance TAAD research.

Main Methods:

  • Creation of imageTAAD, a dataset comprising 120 cases annotated by medical experts with 35 foreground classes relevant to TAAD diagnosis, prognosis, and surgical planning.
  • Identification of four key clinical features for clinically-oriented evaluation of TAAD segmentation.
  • Development of SegTAAD, a segmentation method leveraging domain knowledge regarding the importance of boundaries and the location of tears relative to TL and FL.

Main Results:

  • SegTAAD achieved state-of-the-art (SOTA) performance on the imageTAAD dataset, outperforming other methods in overall DSC score, 95% Hausdorff distance, and the four clinical features.
  • Experimental results demonstrated that SegTAAD effectively utilizes domain knowledge to enhance segmentation accuracy.
  • A key finding was that a higher Dice Similarity Coefficient (DSC) score does not always correlate with improved accuracy in clinical feature extraction.

Conclusions:

  • The imageTAAD dataset and SegTAAD method represent a significant advancement in automatic segmentation for Type-A aortic dissection.
  • The proposed clinically-oriented evaluation provides a more relevant assessment of segmentation performance for clinical utility.
  • The study highlights the importance of integrating domain knowledge into segmentation models and suggests further research into the relationship between segmentation metrics and clinical feature accuracy.