Brain tumor segmentation in multimodal MRI via pixel-level and feature-level image fusion

Liu, Yu; Mu, Fuhao; Cheng, Juan; Li, Chang; Chen, Xun

doi:10.3389/fnins.2022.1000587

Cited by 16 publications

(10 citation statements)

References 54 publications

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“…Liu et al [7] presented a study wherein they employed a pixel-level and feature-level image fusion approach to address the challenge of brain tumor segmentation in multimodal MRI, as discussed in their published work. The proposed technique was evaluated on a total of 369 cases sourced from the BraTS 2020 dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to the National Brain Tumor Society (NBTS), an estimated 90,000 individuals in the United States are affected by primary brain tumors annually, which includes both malignant and nonmalignant instances [5], [6]. It is a matter of concern because an estimated annual mortality rate of approximately 20,000 individuals is attributed to brain tumors and other malignancies affecting the nervous system in the United States [7] [8]. The aforementioned statistics demonstrate a consistent upward trend, underscoring the pressing necessity for the development and implementation of sophisticated and efficient approaches to both diagnosis and treatment.…”

Section: Introductionmentioning

confidence: 99%

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Dual Vision Transformer-DSUNET With Feature Fusion for Brain Tumor Segmentation

Zakariah,

Al-Razgan,

Alfakih

2024

Preprint

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Brain tumors are one of the leading causes of cancer death; screening early is the best strategy to diagnose and treat brain tumors. Magnetic Resonance Imaging (MRI) is extensively utilized for brain tumor diagnosis; nevertheless, achieving improved accuracy and performance, which is a critical challenge in most of the previously reported automated medical diagnostics, is a difficult problem. The study introduces the Dual Vision Transformer-DSUNET model, which incorporates feature fusion techniques to provide precise and efficient differentiation between brain tumors and other brain regions by leveraging multi-modal MRI data. The impetus for this study arises from the necessity of automating the segmentation process of brain tumors in medical imaging, a critical component in the realms of diagnosis and therapy strategy. To tackle this issue the BRATS 2020 dataset is employed, an extensively utilized dataset for the segmentation of brain tumors. This dataset encompasses multi-modal MRI images, including T1-weighted, T2-weighted, T1Gd (contrast-enhanced), and FLAIR modalities. The proposed model incorporates the dual vision idea to comprehensively capture the heterogeneous properties of brain tumors across several imaging modalities. Moreover, the utilization of feature fusion techniques is implemented to augment the amalgamation of data originating from several modalities, hence enhancing the accuracy and dependability of tumor segmentation. The evaluation of the Dual Vision Transformer-DSUNET model's performance is conducted by employing the Dice Coefficient as a prevalent metric for quantifying segmentation accuracy. The results obtained from the experiment exhibit remarkable performance, with Dice Coefficient values of 91.47% for enhanced tumors, 92.38% for core tumors, and 90.88% for edema. The cumulative Dice score for the entirety of the classes is 91.29%. In addition, the model has a high level of accuracy, roughly 99.93%, which underscores its durability and efficacy in the task of segmenting brain tumors. Experimental findings demonstrate the integrity of the suggested architecture, which has quickly improved the detection accuracy of many brain diseases.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dual Vision Transformer-DSUNET With Feature Fusion for Brain Tumor Segmentation

Zakariah,

Al-Razgan,

Alfakih

2024

Preprint

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“…Such modifications make it difficult for comparisons to be made to similar studies that have employed the traditional Dice metric (Equation 1). It also obfuscates the fact that PVS segmentation is a more difficult task compared to other medical imaging problems, where inter-rater Dice scores of 70%+ are commonplace, as opposed to PVS segmentation where inter-rater Dice scores are usually below 50% ( Sudre et al, 2022 , Preprint; Liu et al, 2022 ; Spijkerman et al, 2022 ). This is likely due to the nature of the task, as PVS are small, numerous and occur repeatedly throughout the brain, whilst other lesion detection tasks such as for tumors or stroke lesions require delineation of a single, large and prominent object.…”

Section: Automated Segmentation Of Perivascular Spacesmentioning

confidence: 99%

A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging

Pham

Lynch²,

Spitz³

et al. 2022

Front. Neurosci.

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The glymphatic system is responsible for waste clearance in the brain. It is comprised of perivascular spaces (PVS) that surround penetrating blood vessels. These spaces are filled with cerebrospinal fluid and interstitial fluid, and can be seen with magnetic resonance imaging. Various algorithms have been developed to automatically label these spaces in MRI. This has enabled volumetric and morphological analyses of PVS in healthy and disease cohorts. However, there remain inconsistencies between PVS measures reported by different methods of automated segmentation. The present review emphasizes that importance of voxel-wise evaluation of model performance, mainly with the Sørensen Dice similarity coefficient. Conventional count correlations for model validation are inadequate if the goal is to assess volumetric or morphological measures of PVS. The downside of voxel-wise evaluation is that it requires manual segmentations that require large amounts of time to produce. One possible solution is to derive these semi-automatically. Additionally, recommendations are made to facilitate rigorous development and validation of automated PVS segmentation models. In the application of automated PVS segmentation tools, publication of image quality metrics, such as the contrast-to-noise ratio, alongside descriptive statistics of PVS volumes and counts will facilitate comparability between studies. Lastly, a head-to-head comparison between two algorithms, applied to two cohorts of astronauts reveals how results can differ substantially between techniques.

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“…Feature-level ensemble strategies have also been used for tumor segmentation tasks [ 25 ]. In Liu et al’s study, an architecture called PIF-Net was proposed, based on the combination of features from different MRI modalities [ 26 ]. In Kua et al’s study, brain age estimation with the scope of the regression task was performed using ridge regression and support vector regression (SVR) with ResNet [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI

Terzı

2023

Diagnostics

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This paper proposes ensemble strategies for the deep learning object detection models carried out by combining the variants of a model and different models to enhance the anatomical and pathological object detection performance in brain MRI. In this study, with the help of the novel Gazi Brains 2020 dataset, five different anatomical parts and one pathological part that can be observed in brain MRI were identified, such as the region of interest, eye, optic nerves, lateral ventricles, third ventricle, and a whole tumor. Firstly, comprehensive benchmarking of the nine state-of-the-art object detection models was carried out to determine the capabilities of the models in detecting the anatomical and pathological parts. Then, four different ensemble strategies for nine object detectors were applied to boost the detection performance using the bounding box fusion technique. The ensemble of individual model variants increased the anatomical and pathological object detection performance by up to 10% in terms of the mean average precision (mAP). In addition, considering the class-based average precision (AP) value of the anatomical parts, an up to 18% AP improvement was achieved. Similarly, the ensemble strategy of the best different models outperformed the best individual model by 3.3% mAP. Additionally, while an up to 7% better FAUC, which is the area under the TPR vs. FPPI curve, was achieved on the Gazi Brains 2020 dataset, a 2% better FAUC score was obtained on the BraTS 2020 dataset. The proposed ensemble strategies were found to be much more efficient in finding the anatomical and pathological parts with a small number of anatomic objects, such as the optic nerve and third ventricle, and producing higher TPR values, especially at low FPPI values, compared to the best individual methods.

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Brain tumor segmentation in multimodal MRI via pixel-level and feature-level image fusion

Cited by 16 publications

References 54 publications

Dual Vision Transformer-DSUNET With Feature Fusion for Brain Tumor Segmentation

Dual Vision Transformer-DSUNET With Feature Fusion for Brain Tumor Segmentation

A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging

An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI

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