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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation.

Ümit Budak¹, Yanhui Guo², Erkan Tanyildizi³

¹Electrical and Electronics Engineering Dept., Engineering Faculty, Bitlis Eren University, Bitlis, Turkey.

Medical Hypotheses

|November 1, 2019

Summary

This summary is machine-generated.

This study introduces a deep learning approach using cascaded deep convolutional neural networks for accurate liver and hepatic tumor segmentation in CT images. The novel method achieves high segmentation accuracy, outperforming existing techniques.

Keywords:

Cascaded network Convolutional neural network Encoder-decoder network Liver segmentation

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Area of Science:

Medical image analysis
Deep learning applications in radiology
Computational pathology

Background:

Liver and hepatic tumor segmentation in CT images is challenging due to anatomical variations and indistinct boundaries.
Deep learning methods show promise for improving medical image segmentation accuracy.

Purpose of the Study:

To develop and evaluate a cascaded deep convolutional neural network framework for accurate liver and hepatic tumor segmentation in abdominal CT images.
To address the limitations of existing segmentation methods by improving accuracy and reducing false positives.

Main Methods:

A cascaded classifier framework utilizing two deep encoder-decoder convolutional neural networks (EDCNNs) was developed.
The first EDCNN segments the liver region, serving as input for the second EDCNN, which segments hepatic tumors within the liver ROI.
The model was trained and validated on a public dataset (3DIRCADb).

Main Results:

The proposed EDCNN framework achieved an average DICE score of 95.22% for liver and hepatic tumor segmentation on the test set.
The cascaded approach effectively reduced false positives by segmenting tumors within the liver ROI.
Experimental results demonstrated superior segmentation accuracy compared to several existing methods.

Conclusions:

The cascaded EDCNN framework offers a robust and accurate solution for liver and hepatic tumor segmentation in CT images.
This deep learning approach holds significant potential for enhancing diagnostic capabilities in liver cancer detection and management.
The method's effectiveness on limited image quantities suggests its applicability in real-world clinical scenarios.