Brain Tissue Segmentation using Patch-wise M-net Convolutional Neural Network

Yamanakkanavar, Nagaraj; Lee, Bumshik

doi:10.1109/icce-asia49877.2020.9277239

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…The DSC score for each tissue in Lee et al [35] and in the proposed method is calculated using the average score of the three different planes for the corresponding tissue. The scores for the patch-wise Mnet are taken directly from Yamanakkanavar et al [38].…”

Section: B Comparisons With Other Methodsmentioning

confidence: 99%

SIP-UNet: Sequential Inputs Parallel UNet Architecture for segmentation of Brain Tissues from Magnetic Resonance Images

Prajapati¹

2022

Preprint

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Our work introduces a novel architecture for extracting information from neighboring slices in the image segmentation. The Proposed architecture consists of parallel UNets for each slice/image. These UNets are fused using Residual Network for late fusion. We passed features from central slice only in the residual network so that the features from the image to be segmented will not be lost. We performed segmentation using this architecture on the brain Magnetic Resonance Images (MRI). The processed data is obtained from the Open Access Series of Imaging Studies (OASIS) dataset. Data of each subject is in 3D. We extracted 2D slices/images from each subject and train/test for each view (axial, coronal, sagittal). As the 3D data has neighboring slices, that are similar to each other, we hypothesize and considered before and after slices as neighboring slice for segmentation of central slice.

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Section: B Comparisons With Other Methodsmentioning

confidence: 99%

SIP-UNet: Sequential Inputs Parallel UNet Architecture for segmentation of Brain Tissues from Magnetic Resonance Images

Prajapati¹

2022

Preprint

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“…The DSC score for each tissue is in Lee et al [41] and the proposed method is calculated using the average score of the three different planes for the corresponding tissue. The scores for the patch-wise Mnet are taken directly from Yamanakkanavar et al [42]. From Table 5, it can be seen that the UNet-based deep learning architectures outperform other segmentation models.…”

Section: Comparisons With Other Methodsmentioning

confidence: 99%

SIP-UNet: Sequential Inputs Parallel UNet Architecture for Segmentation of Brain Tissues from Magnetic Resonance Images

Prajapati

Kwon

2022

Mathematics

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Proper analysis of changes in brain structure can lead to a more accurate diagnosis of specific brain disorders. The accuracy of segmentation is crucial for quantifying changes in brain structure. In recent studies, UNet-based architectures have outperformed other deep learning architectures in biomedical image segmentation. However, improving segmentation accuracy is challenging due to the low resolution of medical images and insufficient data. In this study, we present a novel architecture that combines three parallel UNets using a residual network. This architecture improves upon the baseline methods in three ways. First, instead of using a single image as input, we use three consecutive images. This gives our model the freedom to learn from neighboring images as well. Additionally, the images are individually compressed and decompressed using three different UNets, which prevents the model from merging the features of the images. Finally, following the residual network architecture, the outputs of the UNets are combined in such a way that the features of the image corresponding to the output are enhanced by a skip connection. The proposed architecture performed better than using a single conventional UNet and other UNet variants.

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