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Related Experiment Video

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Novel and Innovative Hybrid Technique for Type A Aortic Dissection
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Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning.

Long Cao1, Ruiqiong Shi2, Yangyang Ge1

  • 1Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, PR China.

European Journal of Radiology
|November 5, 2019
PubMed
Summary

A new deep learning model accurately segments Type B aortic dissection (TBAD) using CT scans. This automated method offers efficient and precise measurements of TBAD anatomical features.

Keywords:
Automatic segmentationCTAConvolutional neural networkDeep learningType B aortic dissection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Surgery

Background:

  • Type B aortic dissection (TBAD) requires precise anatomical measurements for effective treatment.
  • Manual segmentation of TBAD from CT images is time-consuming and prone to inter-observer variability.

Purpose of the Study:

  • To develop a fully automated and robust deep learning-based segmentation method for Type B aortic dissection (TBAD).
  • To leverage convolutional neural network (CNN) models for accurate segmentation of the whole aorta, true lumen (TL), and false lumen (FL).

Main Methods:

  • Retrospective collection of preoperative CT angiography (CTA) images from 276 TBAD patients.
  • Establishment of a ground truth database using a manual segmentation protocol.
  • Development and evaluation of three CNN models (single one-task, single multi-task, serial multi-task) using Dice coefficient score (DCS) and volume accuracy.

Main Results:

  • The serial multi-task CNN (CNN3) demonstrated superior performance with high mean DCS values (0.91-0.93) for whole aorta, TL, and FL segmentation.
  • CNN3 achieved excellent agreement between segmented and ground truth volumes, with minimal mean volume differences.
  • The segmentation speed of CNN3 was significantly fast, averaging 0.038 seconds per image.

Conclusions:

  • Deep learning offers a promising approach for accurate and efficient TBAD segmentation.
  • The developed automated method enables precise measurements of TBAD anatomical features.
  • This technology has the potential to improve the clinical management of TBAD patients.