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ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation.

Yeganeh Jalali¹, Mansoor Fateh¹, Mohsen Rezvani¹

¹Faculty of Computer Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran.

Sensors (Basel, Switzerland)

|January 6, 2021

Summary

This summary is machine-generated.

This study introduces a novel deep learning model for automatic lung CT image segmentation, achieving high accuracy. The Res BCDU-Net significantly improves segmentation performance for lung cancer detection applications.

Keywords:

BConvLSTM CT image ResNet-34 U-Net lung segmentation

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Lung CT image segmentation is crucial for lung cancer detection but faces challenges like similar image densities and scanner variations.
Current semi-automatic methods often lack accuracy and have high false-positive rates due to human factors.

Purpose of the Study:

To propose a deep neural network architecture for automatic lung CT image segmentation.
To enhance segmentation accuracy and reduce false positives in medical imaging.

Main Methods:

A modified U-Net architecture (Res BCDU-Net) was developed, replacing the encoder with a pre-trained ResNet-34.
Bidirectional Convolutional Long Short-term Memory (BConvLSTM) was used as an advanced integrator module.
Extensive preprocessing and morphological operations were applied to CT images and ground truths.

Main Results:

The proposed Res BCDU-Net achieved a high Dice coefficient of 97.31% on the LIDC-IDRI lung CT image database.
The method demonstrated effectiveness in automatic lung CT image segmentation.

Conclusions:

The developed deep neural network architecture significantly improves automatic lung CT image segmentation.
The Res BCDU-Net offers a promising solution for accurate and efficient lung cancer detection support.