Correcting and reweighting false label masks in brain tumor segmentation

Cheng, Guohua; Ji, Hongli; He, Li

doi:10.1002/mp.14480

Cited by 11 publications

(12 citation statements)

References 27 publications

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“…Therefore, using a DL algorithm to solve medical image segmentation has always been a research direction with theoretical significance and practical application value. Cheng et al proposed a sample reweighting and label refinement method and introduced a three-dimensional (3D) GAN to efficiently handle mislabel masks (21). Experiments on the BraTS19 dataset showed that this method obtained excellent results in handling the false labels in brain tumor segmentation.…”

Section: Medical Image Segmentationmentioning

confidence: 99%

Medical applications of generative adversarial network: a visualization analysis

Zhang,

Wang,

Zhao

et al. 2023

Acta Radiol

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Background Deep learning (DL) is one of the latest approaches to artificial intelligence. As an unsupervised DL method, a generative adversarial network (GAN) can be used to synthesize new data. Purpose To explore GAN applications in medicine and point out the significance of its existence for clinical medical research, as well as to provide a visual bibliometric analysis of GAN applications in the medical field in combination with the scientometric software Citespace and statistical analysis methods. Material and Methods PubMed, MEDLINE, Web of Science, and Google Scholar were searched to identify studies of GAN in medical applications between 2017 and 2022. This study was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Citespace was used to analyze the number of publications, authors, institutions, and keywords of articles related to GAN in medical applications. Results The applications of GAN in medicine are not limited to medical image processing, but will also penetrate wider and more complex fields, or may be applied to clinical medicine. Eligibility criteria were the full texts of peer-reviewed journals reporting the application of GANs in medicine. Research selections included material published in English between 1 January 2017 and 1 December 2022. Conclusion GAN has been fully applied to the medical field and will be more deeply and widely used in clinical medicine, especially in the field of privacy protection and medical diagnosis. However, clinical applications of GAN require consideration of ethical and legal issues. GAN-based applications should be well validated by expert radiologists.

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Section: Medical Image Segmentationmentioning

confidence: 99%

Medical applications of generative adversarial network: a visualization analysis

Zhang,

Wang,

Zhao

et al. 2023

Acta Radiol

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“…In addition, a residual module for conventional up- and downsampling has been added. The BraTS 2018 dataset was used to train this model, which resulted in a significant improvement in mIoU of 0.75 and a decrease in inference time [ 19 ].…”

Section: Background and Related Workmentioning

confidence: 99%

A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation

et al. 2023

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Background: Medical image segmentation is more complicated and demanding than ordinary image segmentation due to the density of medical pictures. A brain tumour is the most common cause of high mortality. Objectives: Extraction of tumorous cells is particularly difficult due to the differences between tumorous and non-tumorous cells. In ordinary convolutional neural networks, local background information is restricted. As a result, previous deep learning algorithms in medical imaging have struggled to detect anomalies in diverse cells. Methods: As a solution to this challenge, a deep convolutional generative adversarial network for tumour segmentation from brain Magnetic resonance Imaging (MRI) images is proposed. A generator and a discriminator are the two networks that make up the proposed model. This network focuses on tumour localisation, noise-related issues, and social class disparities. Results: Dice Score Coefficient (DSC), Peak Signal to Noise Ratio(PSNR), and Structural Index Similarity (SSIM) are all generally 0.894, 62.084 dB, and 0.88912, respectively. The model’s accuracy has improved to 97 percent, and its loss has reduced to 0.012. Conclusions: Experiments reveal that the proposed approach may successfully segment tumorous and benign tissues. As a result, a novel brain tumour segmentation approach has been created.

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“…All images in the two datasets have been coregistered, interpolated, and skull-stripped. The training set is publicly accessible, but the groundtruth for validation set and test set is not open to access, BraTS 2015 1 and BraTS 2018 2 evaluation metrics are obtained by online evaluation.…”

Section: Datasetsmentioning

confidence: 99%

“…Gliomas are the most common brain tumors among the adults and one of the most deadly cancers. The clinical population with aggressive high‐grade gliomas (HGGs) usually has a life expectancy less than 2 years and requires immediate treatment 1 . As a nonintrusive imaging method, magnetic resonance imaging (MRI) is a powerful diagnostic approach for brain tumors.…”

Section: Introductionmentioning

confidence: 99%

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MSFR‐Net: Multi‐modality and single‐modality feature recalibration network for brain tumor segmentation

Jiang

et al. 2022

Medical Physics

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Background: Accurate and automated brain tumor segmentation from multimodality MR images plays a significant role in tumor treatment. However, the existing approaches mainly focus on the fusion of multi-modality while ignoring the correlation between single-modality and tumor subcomponents. For example, T2-weighted images show good visualization of edema, and T1-contrast images have a good contrast between enhancing tumor core and necrosis. In the actual clinical process, professional physicians also label tumors according to these characteristics. We design a method for brain tumors segmentation that utilizes both multi-modality fusion and single-modality characteristics. Methods: A multi-modality and single-modality feature recalibration network (MSFR-Net) is proposed for brain tumor segmentation from MR images. Specifically, multi-modality information and single-modality information are assigned to independent pathways. Multi-modality network explicitly learns the relationship between all modalities and all tumor sub-components. Single-modality network learns the relationship between single-modality and its highly correlated tumor subcomponents. Then, a dual recalibration module (DRM) is designed to connect the parallel single-modality network and multi-modality network at multiple stages. The function of the DRM is to unify the two types of features into the same feature space. Results: Experiments on BraTS 2015 dataset and BraTS 2018 dataset show that the proposed method is competitive and superior to other state-of -the-art methods. The proposed method achieved the segmentation results with Dice coefficients of 0.86 and Hausdorff distance of 4.82 on BraTS 2018 dataset, with dice coefficients of 0.80, positive predictive value of 0.76, and sensitivity of 0.78 on BraTS 2015 dataset. Conclusions: This work combines the manual labeling process of doctors and introduces the correlation between single-modality and the tumor subcomponents into the segmentation network. The method improves the segmentation performance of brain tumors and can be applied in the clinical practice. The code of the proposed method is available at: https://github.com/xiangQAQ/ MSFR-Net.

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Correcting and reweighting false label masks in brain tumor segmentation

Cited by 11 publications

References 27 publications

Medical applications of generative adversarial network: a visualization analysis

Medical applications of generative adversarial network: a visualization analysis

A Novel Generative Adversarial Network-Based Approach for Automated Brain Tumour Segmentation

MSFR‐Net: Multi‐modality and single‐modality feature recalibration network for brain tumor segmentation

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