Abstract
Image segmentation is a challenging problem in medical applications. Medical imaging has become an integral part of machine learning research, as it enables inspecting interior human body with no surgical intervention. Much research has been conducted to study brain segmentation. However, prior studies usually employ one-stage models to segment brain tissues, which could lead to a significant information loss. In this paper, we propose a multi-stage Generative Adversarial Network (<inline-formula> <tex-math notation="LaTeX">$GAN$ </tex-math></inline-formula>) model to resolve existing issues of one-stage models. To do this, we apply a <i>coarse-to-fine</i> method to improve brain segmentation using a multi-stage <inline-formula> <tex-math notation="LaTeX">$GAN$ </tex-math></inline-formula>. In the first stage, our model generates a <i>coarse</i> outline for both the background and brain tissues. Then, in the second stage, the model generates a <i>refine</i> outline for the white matter (<inline-formula> <tex-math notation="LaTeX">$WM$ </tex-math></inline-formula>), gray matter (<inline-formula> <tex-math notation="LaTeX">$GM$ </tex-math></inline-formula>), and cerebrospinal fluid (<inline-formula> <tex-math notation="LaTeX">$CSF$ </tex-math></inline-formula>). We perform a fusion of the <i>coarse</i> and <i>refine</i> outlines to achieve high results. Despite using very limited data, we obtain an improved Dice Coefficient (DC) accuracy of up to 5% compared to one-stage models. We conclude that our model is more efficient and accurate in practice for brain segmentation of both infants and adults. In addition, we observe that our multi-stage model is 2.69–13.93 minutes faster than prior models. Moreover, our multi-stage model achieves higher performance with only a few-shot learning, in which only limited labeled data is available. Therefore, for medical images, our solution is applicable to a wide range of image segmentation applications for which convolution neural networks and one-stage methods have failed. This helps to advance the process of analyzing brain images, thus providing many advantages to the healthcare system, especially in critical health situations where urgent intervention is needed.
Highlights
Magnetic resonance imaging (M RI) employs a magnetic field to generate detailed images of tissues without using harmful radiations [1], [2]
In this paper, we proposed amulti-stage generative adversarial network (GAN ) model for brain segmentation that generates a coarse outline for both background and brain tissues
Our model generates an outline for white matter (W M ), gray matter (GM ), and cerebrospinal fluid (CSF )
Summary
Magnetic resonance imaging (M RI) employs a magnetic field to generate detailed images of tissues without using harmful radiations [1], [2]. These images tend to be segmented manually, a process that is considered timeconsuming and clinically expensive [3]. Training deep learning models requires large sets of labeled images [6]. Due to the limited sets of data in medical applications [7], [8], semi-supervised learning techniques has been used to address this issue by means of unlabeled image [9], [10]. A. SEMI-SUPERVISED LEARNING Training a deep model using a small datasets may cause overfitting [11], [19]. Training deep models using both labeled and unlabeled data encourages neural networks to have a similar distribution [22], [23].
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