Dataset Information

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities.

ABSTRACT: Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained on the BraTS and JC datasets, respectively, whereas the fine-tuning models are developed from the BraTS model and fine-tuned using the JC dataset. Our results show that the Dice coefficient score of the JC model for the test portion of the JC dataset was 0.779 ± 0.137, whereas that of the BraTS model was lower (0.717 ± 0.207). The mean Dice coefficient score of the fine-tuning model was 0.769 ± 0.138. There was a significant difference between the BraTS and JC models (p < 0.0001) and the BraTS and fine-tuning models (p = 0.002); however, no significant difference between the JC and fine-tuning models (p = 0.673). As our fine-tuning method requires fewer than 20 cases, this method is useful even in a facility where the number of glioma cases is small.

SUBMITTER: Takahashi S

PROVIDER: S-EPMC8003655 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities.

Takahashi Satoshi S Takahashi Masamichi M Kinoshita Manabu M Miyake Mototaka M Kawaguchi Risa R Shinojima Naoki N Mukasa Akitake A Saito Kuniaki K Nagane Motoo M Otani Ryohei R Higuchi Fumi F Tanaka Shota S Hata Nobuhiro N Tamura Kaoru K Tateishi Kensuke K Nishikawa Ryo R Arita Hideyuki H Nonaka Masahiro M Uda Takehiro T Fukai Junya J Okita Yoshiko Y Tsuyuguchi Naohiro N Kanemura Yonehiro Y Kobayashi Kazuma K Sese Jun J Ichimura Koichi K Narita Yoshitaka Y Hamamoto Ryuji R

Cancers 20210319 6

Machine learning models for automated magnetic resonance image segmentation may be useful in aiding glioma detection. However, the image differences among facilities cause performance degradation and impede detection. This study proposes a method to solve this issue. We used the data from the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) and the Japanese cohort (JC) datasets. Three models for tumor segmentation are developed. In our methodology, the BraTS and JC models are trained ...[more]

PMID: 33808802

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Project description:BackgroundImage segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development.PurposeTo determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation.MethodsWe conducted a systematic review of 572 brain tumour segmentation studies during 2015-2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score).Statistical testsWe compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour.ResultsWe found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation.ConclusionU-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.

Project description:Transcranial brain stimulation (TBS) has been established as a method for modulating and mapping the function of the human brain, and as a potential treatment tool in several brain disorders. Typically, the stimulation is applied using a one-size-fits-all approach with predetermined locations for the electrodes, in electric stimulation (TES), or the coil, in magnetic stimulation (TMS), which disregards anatomical variability between individuals. However, the induced electric field distribution in the head largely depends on anatomical features implying the need for individually tailored stimulation protocols for focal dosing. This requires detailed models of the individual head anatomy, combined with electric field simulations, to find an optimal stimulation protocol for a given cortical target. Considering the anatomical and functional complexity of different brain disorders and pathologies, it is crucial to account for the anatomical variability in order to translate TBS from a research tool into a viable option for treatment. In this article we present a new method, called CHARM, for automated segmentation of fifteen different head tissues from magnetic resonance (MR) scans. The new method compares favorably to two freely available software tools on a five-tissue segmentation task, while obtaining reasonable segmentation accuracy over all fifteen tissues. The method automatically adapts to variability in the input scans and can thus be directly applied to clinical or research scans acquired with different scanners, sequences or settings. We show that an increase in automated segmentation accuracy results in a lower relative error in electric field simulations when compared to anatomical head models constructed from reference segmentations. However, also the improved segmentations and, by implication, the electric field simulations are affected by systematic artifacts in the input MR scans. As long as the artifacts are unaccounted for, this can lead to local simulation differences up to 30% of the peak field strength on reference simulations. Finally, we exemplarily demonstrate the effect of including all fifteen tissue classes in the field simulations against the standard approach of using only five tissue classes and show that for specific stimulation configurations the local differences can reach 10% of the peak field strength.

Project description:BackgroundGlioma is a primary malignant craniocerebral tumor commonly found in the central nervous system. According to research, preoperative diagnosis of glioma and a full understanding of its imaging features are very significant. Still, the traditional segmentation methods of image dispensation and machine wisdom are not acceptable in glioma segmentation. This analysis explores the potential of magnetic resonance imaging (MRI) brain tumor images as an effective segmentation method of glioma.MethodsThis study used 200 MRI images from the affiliated hospital and applied the 2-dimensional residual block UNet (2DResUNet). Features were extracted from input images using a 2×2 kernel size (64-kernel) 1-step 2D convolution (Conv) layer. The 2DDenseUNet model implemented in this study incorporates a ResBlock mechanism within the UNet architecture, as well as a Gaussian noise layer for data augmentation at the input stage, and a pooling layer for replacing the conventional 2D convolutional layers. Finally, the performance of the proposed protocol and its effective measures in glioma segmentation were verified.ResultsThe outcomes of the 5-fold cross-validation evaluation show that the proposed 2DResUNet and 2DDenseUNet structure has a high sensitivity despite the slightly lower evaluation result on the Dice score. At the same time, compared with other models used in the experiment, the DM-DA-UNet model proposed in this paper was significantly improved in various indicators, increasing the reliability of the model and providing a reference and basis for the accurate formulation of clinical treatment strategies. The method used in this study showed stronger feature extraction ability than the UNet model. In addition, our findings demonstrated that using generalized die harm and prejudiced cross entropy as loss functions in the training process effectively alleviated the class imbalance of glioma data and effectively segmented glioma.ConclusionsThe method based on the improved UNet network has obvious advantages in the MRI brain tumor portrait segmentation procedure. The result showed that we developed a 2D residual block UNet, which can improve the incorporation of glioma segmentation into the clinical process.

Dataset Information

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities.

Publications

Fine-Tuning Approach for Segmentation of Gliomas in Brain Magnetic Resonance Images with a Machine Learning Method to Normalize Image Differences among Facilities.

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