Brain Tumor Segmentation and Tumor Prediction Using 2D-VNet Deep Learning Architecture

Rastogi, Deependra; Johri, Prashant; Tiwari, Varun

doi:10.1109/smart52563.2021.9676317

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…Downsampling is often used to broaden the receptive field. We used the pre-build 2D-Vnet method trained on the HGG BraTS2020 dataset using python3 and the output used for comparative analysis (Rastogi et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…Despite the success, reliability, and accessibility of different methods for performing brain tumor segmentation using deep learning methods have not been systematically benchmarked. In this current study, we address the existing gap by providing a comprehensive benchmarking analysis that compares the accuracy and robustness of widely used brain tumor segmentation methods.This study aims to systematically evaluate the reliability and accessibility of four different widely used automated deep learning methods for brain tumor segmentation i.e., CaPTk software (Rathore et al, 2018), 2DVNet (Rastogi et al, 2021), Ensemble UNets (Y. Zhang et al, 2021), and ResNet50 (Thhntb.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Benchmarking of CNN-Based Tumor Segmentation Methods Using Multimodal MRI Data

Kundal,

Rao,

Majumdar

et al. 2024

Preprint

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Magnetic resonance imaging (MRI) is become an essential and a frontline technique in the detection of brain tumor. However, manual segmentation of tumor from MRI scans is a time-consuming and labour-intensive process. There is a prevalent trend in employing fully automated methods for accurate tumor segmentation using MRI scans. The precision in brain tumor segmentation is essential for the better diagnosis, treatment and prognosis. This study focuses on benchmarking and evaluating the performance of four widely used convolutional neural network (CNN) based brain tumor segmentation methods CaPTk, 2DVNet, EnsembleUNets, and ResNet50. We used 1251 multimodal MRI scans from the BraTS2021 dataset, which encompasses T1, T2, T1ce, and Flair imaging modalities. We compared the performance of these CNN-based methods against a reference set of previously segmented images obtained with the assistance of radiologists. The evaluation encompasses direct utilization of segmented outputs and also by employing radiomic features. Performance evaluation with direct segments method using Dice Similarity Coefficient score (DSC) and Hausdorff Distance (HD) suggested better performance of EnsembleUNets with DSC and HD of 0.93 and 18 respectively, outperforming the other methods. Comparative analysis using radiomics features also revealed that EnsembleUNets is the most precise segmentation method as compared to CaPTk, 2DVNet, and ResNet50. EnsembleUNets achieved Concordance Correlation Coefficient (CCC), Total Deviation Index (TDI), and Root Mean Square Error (RMSE) of 0.79, 1.14 and 0.53 respectively and outperformed its counterparts. These findings contribute valuable insight into the comparative efficacy of EnsembleUNets, facilitating informed decision for accurate brain tumor segmentation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comprehensive Benchmarking of CNN-Based Tumor Segmentation Methods Using Multimodal MRI Data

Kundal,

Rao,

Majumdar

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Their CNN-based approach outperformed traditional methods and showed improved sensitivity and specificity parameters. Rastogi, Johari and Tiwari [24] proposed the use of a 2D-VNet model for brain segmentation and prediction for effective discrimination between tumor and healthy tissue. However, the lack of detailed information about model limitations poses challenges for further improvement.…”

Section: IImentioning

confidence: 99%

Brain Tumor Growth Prediction Based on Segmentation Using Resolution Convolution Network

Srivastava

2024

IJRASET

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This study presents a novel approach to brain tumor growth prediction using Resolution Convolution Network (RCN) segmentation. The method involves precise segmentation of brain tumor regions from magnetic resonance imaging (MRI) scans followed by application of RCN to predict growth. The RCN architecture integrates high-resolution convolutional layers, which facilitates the extraction of detailed features that are crucial for accurate segmentation and subsequent prediction. Performance evaluation is performed on a comprehensive dataset of MRI brain scans

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“…As a result, it has produced impressive results and made outstanding contributions to the clinical diagnosis and care of patients with brain tumors. In this [15] the researcher used 2D V-net K for tumor brain segmentation and enhancing predication they used data set BRATS2020.…”

Section: Related Workmentioning

confidence: 99%

“…and therefore can extract the features of the three regions with greater accuracy. As a result, it has produced impressive results and made outstanding contributions to the clinical diagnosis and care of patients with brain tumors [15]. There are a lot of studies involving deep learning techniques and explaining them in details [16]- [18].…”

Section: Introductionmentioning

confidence: 99%

MRI Semantic Segmentation based on Optimize V-net with 2D Attention

Al-azzwi,

Nazarov

2023

Adv. sci. technol. eng. syst. j.

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Over the past ten years, deep learning models have considerably advanced research in artificial intelligence, particularly in the segmentation of medical images. One of the key benefits of medical picture segmentation is that it allows for a more accurate analysis of anatomical data by separating only pertinent areas. Numerous studies revealed that these models could make accurate predictions and provide results that were on par with those of doctors. In this study, we investigate different methods of deep learning with medical image segmentation, like the V-net and U-net models. Improve the V-net model by adding attention in 2D with a decoder to get high performance through the training model. Using tumors of severe forms, size, and location, we downloaded the BRAST 2018 data set from Kaggle and manually segmented structural T1, T1ce, T2, and Flair MRI images. To enhance segmentation performance, we also investigated several benchmarking and preprocessing procedures. It's significant to note that our model was applied on Colab-Google for 35 epochs with a batch size of 8. In conclusion, we offer a memory-effective and effective tumor segmentation approach to aid in the precise diagnosis of oncological brain diseases. We have tested residual connections, decoder attention, and deep supervision loss in a comprehensive ablation study. Also, we looked for the U-Net encoder and decoder depth, convolutional channel count, and post-processing approach.

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Brain Tumor Segmentation and Tumor Prediction Using 2D-VNet Deep Learning Architecture

Cited by 11 publications

References 16 publications

Comprehensive Benchmarking of CNN-Based Tumor Segmentation Methods Using Multimodal MRI Data

Comprehensive Benchmarking of CNN-Based Tumor Segmentation Methods Using Multimodal MRI Data

Brain Tumor Growth Prediction Based on Segmentation Using Resolution Convolution Network

MRI Semantic Segmentation based on Optimize V-net with 2D Attention

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