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MULTI-SCALE SEGMENTATION USING DEEP GRAPH CUTS: ROBUST LUNG TUMOR DELINEATION IN MVCBCT.

Xiaodong Wu^1,2, Zisha Zhong^1,2, John Buatti²

¹Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|November 28, 2019

Summary

This study introduces a novel deep learning approach for medical image segmentation by framing it as a Markov Random Field (MRF) energy minimization problem. The method efficiently segments lung tumors using a minimum s-t cut algorithm on graph networks.

Keywords:

Deep graph cuts deep networks lung tumor segmentation multi-scale image segmentation

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Area of Science:

Medical Image Analysis
Deep Learning
Computer Vision

Background:

Deep learning demonstrates significant advancements in medical image analysis.
Accurate segmentation of anatomical structures and pathologies is crucial for diagnosis and treatment planning.

Purpose of the Study:

To develop an efficient and exact method for multi-scale image segmentation using deep networks.
To apply the proposed method to lung tumor segmentation in cone-beam computed tomography (CBCT) data.

Main Methods:

Formulating multi-scale segmentation as a Markov Random Field (MRF) energy minimization problem within a deep network (graph).
Solving the MRF energy minimization exactly and efficiently using a minimum s-t cut algorithm on a constructed graph.

Main Results:

The proposed deep network-based MRF approach achieves efficient and exact multi-scale segmentation.
Successful application demonstrated on lung tumor segmentation using 38 mega-voltage cone-beam computed tomography (CBCT) datasets.

Conclusions:

The integration of deep networks with MRF energy minimization offers a powerful framework for medical image segmentation.
This method provides a robust solution for challenging segmentation tasks like lung tumor identification in CBCT imaging.