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Related Experiment Video

Updated: Apr 28, 2026

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Deep learning-based segmentation and quantification of pulmonary masses on apparent diffusion coefficient maps: a

Lan Ding1, Tao Chen2, Qining Gao3

  • 1School of Biomedical Engineering, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 511436, China; School of Communication and Electronic Engineering, Jishou University, Jishou 416000, China.

European Journal of Radiology
|April 26, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model, ADCSegNet, accurately segments pulmonary masses on apparent diffusion coefficient (ADC) maps. This automated method shows high repeatability and agreement with expert radiologists for ADC quantification.

Keywords:
Apparent diffusion coefficient (ADC)Automatic segmentationDeep learningMRIMulticentre studyPulmonary massReproducibility

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Pulmonary mass segmentation on apparent diffusion coefficient (ADC) maps is crucial for diagnosis and treatment monitoring.
  • Manual segmentation can be time-consuming and subject to inter-observer variability.
  • Automated segmentation methods are needed to improve efficiency and consistency.

Purpose of the Study:

  • To develop and validate ADCSegNet, a deep learning model for automatic pulmonary mass segmentation on ADC maps.
  • To assess the repeatability and agreement of automated ADC quantification compared to manual measurements.
  • To evaluate the generalisability of ADCSegNet across different imaging centers.

Main Methods:

  • ADCSegNet, a deep learning model, was trained and tested on pulmonary ADC maps from multiple centers.
  • Segmentation accuracy was measured using the Dice similarity coefficient (DSC).
  • Agreement and repeatability of ADC measurements were assessed using Bland-Altman analysis, ICC(2,1), RDC, and CCC.

Main Results:

  • ADCSegNet achieved high internal (DSC 0.843) and external (DSC 0.701) segmentation accuracy.
  • Automated ADC quantification showed excellent agreement with senior radiologists (mean difference 0.00).
  • ADCSegNet demonstrated higher repeatability than radiologists in test-retest analyses (CCC 0.931 vs. 0.908).

Conclusions:

  • ADCSegNet provides accurate and reproducible automated segmentation of pulmonary masses on ADC maps.
  • The model outperforms state-of-the-art backbones and shows strong agreement with expert readers.
  • ADCSegNet offers a reliable tool for automated ADC quantification in clinical practice.