Improved Brain Segmentation Using Pixel Separation and Additional Segmentation Features

Khaled, Afifa; Own, Chung-Ming; Tao, Wenyuan; Ghaleb, Taher Ahmed

doi:10.1007/978-3-030-60290-1_7

Cited by 9 publications

(16 citation statements)

References 19 publications

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“…This result suggests that BIM has contributed the improved distinction between the boundaries for GM . However, for segmenting CSF and W M , we observe that the result of our model was 1% lower than those proposed in [17] and [38], which is likely due to the inclusion of some irrelevant information of the GM in CSF and W M . This encourages us to further improve the boundary detection to carefully account for the features missed by our current model.…”

Section: Analysis Of the Resultscontrasting

confidence: 77%

“…However, the problem of unclear boundaries between (W M ) and (GM ) remains challenging due to the low contrast of MRI images. This problem has also been studied extensively [17][18][19]. The main focus of these studies was on mixed features between W M and GM , in which the boundary information of these two regions is unclear and hard to identify.…”

Section: Boundary Detectionmentioning

confidence: 99%

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Learning to Detect Boundary Information for Brain Image Segmentation

Khaled

Han

Ghaleb

2022

Preprint

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MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix properties of different brain regions. Hence, to alleviate such a problem, image boundary detection plays a vital role in medical image segmentation, and brain segmentation in particular, as unclear boundaries can worsen brain segmentation results. Yet, given the low quality of brain images, boundary detection in the context of brain image segmentation remains challenging. Despite the research invested to improve boundary detection and brain segmentation, these two problems were addressed independently, i.e., little attention was paid to applying boundary detection to brain segmentation tasks. Therefore, in this paper, we propose a boundary detection-based model for brain image segmentation. To this end, we first design a boundary segmentation network for detecting and segmenting images brain tissues. Then, we design a boundary information module (BIM) to distinguish boundaries from the three different brain tissues. After that, we add a boundary attention gate (BAG) to the encoder output layers of our transformer to capture more informative local details. We evaluate our proposed model on two datasets of brain tissue images, including infant and adult brains. The extensive evaluation experiments of our model show better performance (a Dice Coefficient (DC) accuracy of up to 5.3% compared to the state-of-the-art models) in detecting and segmenting brain tissue images.

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Section: Analysis Of the Resultscontrasting

confidence: 77%

Section: Boundary Detectionmentioning

confidence: 99%

Learning to Detect Boundary Information for Brain Image Segmentation

Khaled

Han

Ghaleb

2022

Preprint

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“…Hence, automated segmentation of infant and adult brain images has received a substantial research attention [4], [5]. However, 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].…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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 (GAN ) model to resolve existing issues of one-stage models. To do this, we apply a coarse-to-fine method to improve brain segmentation using a multi-stage GAN . In the first stage, our model generates a coarse outline for both the background and brain tissues. Then, in the second stage, the model generates a refine outline for the white matter (W M ), gray matter (GM ), and cerebrospinal fluid (CSF ). We perform a fusion of the coarse and refine 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.

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“…Automated segmentation of infant and adult brain has received a substantial research attentions [4] [5]. Training deep models need for large sets of labeled images [6]. Due to the small data sets in the medical application [7] [8], semi supervisor learning approaches solved this problem by using unlabeled image [9] [10].…”

Section: Introductionmentioning

confidence: 99%

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

Khaled¹,

Han²

2021

Preprint

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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.

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Improved Brain Segmentation Using Pixel Separation and Additional Segmentation Features

Cited by 9 publications

References 19 publications

Learning to Detect Boundary Information for Brain Image Segmentation

Learning to Detect Boundary Information for Brain Image Segmentation

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

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

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