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Automatic segmentation of the uterus on MRI using a convolutional neural network.

Yasuhisa Kurata1, Mizuho Nishio2, Aki Kido3

  • 1Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan; Department of Diagnostic Radiology, Kobe City Medical Center General Hospital, 2-1-1, Minatojimaminamimachi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan.

Computers in Biology and Medicine
|September 16, 2019
PubMed
Summary

A modified U-net model achieved clinically feasible, fully automatic uterine segmentation on MRI. This AI-powered approach accurately segmented uteruses, regardless of common uterine disorders, demonstrating its potential in clinical practice.

Keywords:
CNNConvolutional neural networkSegmentationU-netUterus

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Urology

Background:

  • Accurate uterine segmentation on MRI is crucial for diagnosing and managing various gynecological conditions.
  • Traditional manual segmentation is time-consuming and subject to inter-observer variability.

Purpose of the Study:

  • To evaluate the clinical feasibility of a U-net for fully automatic uterine segmentation on MRI.
  • To assess the performance of automatic uterine segmentation in the presence of major uterine disorders.

Main Methods:

  • A modified U-net architecture was developed for automatic uterine segmentation.
  • 122 female patients with and without uterine disorders (cancer, leiomyoma) were included.
  • Segmentation accuracy was quantitatively evaluated using Dice Similarity Coefficient (DSC) and Mean Absolute Distance (MAD), with manual segmentation as the gold standard.

Main Results:

  • The U-net model achieved a mean DSC of 0.82 for all patients.
  • Segmentation performance (DSC and MAD) was comparable between uteruses with and without disorders (p > 0.19).
  • Visual evaluation by radiologists showed no significant difference in segmentation quality based on the presence of uterine disorders.

Conclusions:

  • Fully automatic uterine segmentation using the modified U-net is clinically feasible.
  • The U-net model's segmentation performance is not significantly affected by the presence of common uterine disorders.