Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Network

Version 1 : Received: 30 November 2021 / Approved: 2 December 2021 / Online: 2 December 2021 (09:57:51 CET)
Version 2 : Received: 5 December 2021 / Approved: 6 December 2021 / Online: 6 December 2021 (14:33:23 CET)

How to cite: Khaled, A.; Han, J. Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Network. Preprints 2021, 2021120025 (doi: 10.20944/preprints202112.0025.v2). Khaled, A.; Han, J. Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Network. Preprints 2021, 2021120025 (doi: 10.20944/preprints202112.0025.v2).

Abstract

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

Keywords

Brain segmentation; Coarse-to-fine; Gen- erative Adversarial Network; Semi-supervised learning; Multi-stage method

Subject

ENGINEERING, Other

Comments (1)

Comment 1
Received: 6 December 2021
Commenter: Afifa Khaled
Commenter's Conflict of Interests: Author
Comment: 1- We introduce a novel generated loss to encourage G to generate real data.

2- We submitted our paper for review on BMC Bioinformatics journal 
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