Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation

Murugesan, Gowtham Krishnan; Nalawade, Sahil; Ganesh, Chandan; Wagner, Ben; Yu, Fang; Fei, Baowei; Madhuranthakam, Ananth J.; Maldjian, Joseph A.

doi:10.1101/760124

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Determining the optimal method to combine base classifiers is critical to the performance of the final outcomes from ensemble models 38 . The most widely used combination rule is naive averaging, which has demonstrated significant improvement in predictive accuracy for the semantic segmentation of medical imaging 39–43 . AtlasNet 44 used an ensemble of six convolutional neural networks to evaluate the extent of interstitial lung disease in systemic sclerosis; this showed a performance comparable to that of radiologists.…”

Section: Introductionmentioning

confidence: 99%

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography

et al. 2023

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BackgroundInvasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi‐automatic segmentation tools require labor‐intensive and time‐consuming manual correction, limiting their application in the catheterization room.PurposeThis study aims to propose rank‐based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep‐learning segmentation of ICA.MethodsTwo selective ensemble methods proposed in this work integrated the weighted ensemble approach with per‐image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo‐ground truth generated from a meta‐learner (ESEN). Five‐fold cross‐validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients.ResultsThe selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one‐sixth of a second.ConclusionProposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real‐time QCA‐based diagnostic methods in routine clinical settings.

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Section: Introductionmentioning

confidence: 99%

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography

et al. 2023

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“…Ensembling is often adapted for the task of brain tumour segmentation and has the advantage of improving both results and performance [47]- [49]. We propose a lightweight ensemble consisting of as few as two networks, each selectively trained on the training set.…”

Section: B Methodologymentioning

confidence: 99%

Brain Tumour Image Segmentation Using Deep Networks

et al. 2020

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Automated segmentation of brain tumour from multimodal MR images is pivotal for the analysis and monitoring of disease progression. As gliomas are malignant and heterogeneous, efficient and accurate segmentation techniques are used for the successful delineation of tumours into intra-tumoural classes. Deep learning algorithms outperform on tasks of semantic segmentation as opposed to the more conventional, context-based computer vision approaches. Extensively used for biomedical image segmentation, Convolutional Neural Networks have significantly improved the state-of-the-art accuracy on the task of brain tumour segmentation. In this paper, we propose an ensemble of two segmentation networks: a 3D CNN and a U-Net, in a significant yet straightforward combinative technique that results in better and accurate predictions. Both models were trained separately on the BraTS-19 challenge dataset and evaluated to yield segmentation maps which considerably differed from each other in terms of segmented tumour sub-regions and were ensembled variably to achieve the final prediction. The suggested ensemble achieved dice scores of 0.750, 0.906 and 0.846 for enhancing tumour, whole tumour, and tumour core, respectively, on the validation set, performing favourably in comparison to the state-of-the-art architectures currently available.

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“…Murugesan et al. ( 19 ) presented a multidimensional and multiresolution ensemble neural network for brain tumor segmentation and trained a traditional machine learning model for survival prediction. Specifically, an ensemble of pre-trained neural networks such as DenseNET-169, SERESNEXT-101, and SENet-154 was utilized to segment whole, core, and enhanced tumors.…”

Section: Related Workmentioning

confidence: 99%

A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor

et al. 2022

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Magnetic resonance imaging is the most generally utilized imaging methodology that permits radiologists to look inside the cerebrum using radio waves and magnets for tumor identification. However, it is tedious and complex to identify the tumorous and nontumorous regions due to the complexity in the tumorous region. Therefore, reliable and automatic segmentation and prediction are necessary for the segmentation of brain tumors. This paper proposes a reliable and efficient neural network variant, i.e., an attention-based convolutional neural network for brain tumor segmentation. Specifically, an encoder part of the UNET is a pre-trained VGG19 network followed by the adjacent decoder parts with an attention gate for segmentation noise induction and a denoising mechanism for avoiding overfitting. The dataset we are using for segmentation is BRATS’20, which comprises four different MRI modalities and one target mask file. The abovementioned algorithm resulted in a dice similarity coefficient of 0.83, 0.86, and 0.90 for enhancing, core, and whole tumors, respectively.

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Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation

Cited by 6 publications

References 20 publications

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography

Brain Tumour Image Segmentation Using Deep Networks

A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor

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