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Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet.

Fırat Özcan1, Osman Nuri Uçan2, Songül Karaçam3

  • 1Department of Mechatronics Engineering, Faculty of Technology, Kırklareli University, Kayalı Campus, 39100 Kırklareli, Turkey.

Bioengineering (Basel, Switzerland)
|February 25, 2023
PubMed
Summary

This study introduces the AIM-Unet model for accurate automatic liver and tumor segmentation in CT scans. The novel approach significantly improves segmentation performance, aiding physicians in diagnosis.

Keywords:
AIM-UnetU-Netcomputed tomography (CT)deep learningliver segmentationtumor segmentation

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Artificial Intelligence in Medicine

Background:

  • Liver segmentation in computed tomography (CT) images is challenging due to variations in shape, border, and density.
  • Accurate segmentation is crucial for diagnosing liver conditions and planning treatments.

Purpose of the Study:

  • To propose the Adding Inception Module-Unet (AIM-Unet) model for automated liver and liver tumor segmentation in abdominal CT scans.
  • To evaluate the performance of the AIM-Unet model against specialist-marked segmentations using established metrics.

Main Methods:

  • Developed a hybrid deep learning model, AIM-Unet, integrating Unet and Inception architectures.
  • Trained and tested the model on four diverse liver CT datasets, including open-source (CHAOS, LIST, 3DIRCADb) and a custom dataset.
  • Quantitatively assessed segmentation accuracy using Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (JSC), and Accuracy (ACC).

Main Results:

  • The AIM-Unet model achieved high liver segmentation performance on the CHAOS dataset, with DSC of 97.86%, JSC of 96.10%, and ACC of 99.75%.
  • Tumor segmentation yielded DSC metrics of 75.6% on the LiST dataset and 65.5% on the 3DIRCADb dataset.
  • Performance comparison with previous studies demonstrated the effectiveness of the proposed AIM-Unet model.

Conclusions:

  • The AIM-Unet model shows significant potential as an auxiliary tool for physicians in liver segmentation and liver tumor detection from CT scans.
  • The developed model's architecture is adaptable for segmentation tasks in other organs and medical imaging fields.
  • This research contributes to advancing computer-assisted diagnosis in medical imaging.