Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression

Randhawa, Ramandeep S.; Modi, Ankit; Jain, Parag; Warier, Prashant

doi:10.1007/978-3-319-55524-9_7

Cited by 31 publications

(19 citation statements)

References 5 publications

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“…To alleviate the imbalance, Lun and Hsu (2016) proposed a re-weighted loss function. Randhawa et al (2016) also modified the cross-entropy loss function so that the segmentations at tumor edges could be improved. Instead of analysing multi-modal MRIs in 2D, the DeepMedic approach ( Kamnitsas et al, 2016a ) performs segmentation of tumors in 3D while using extended residual connections.…”

Section: Related Workmentioning

confidence: 99%

Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks

Liang

Bentley

Rueckert

2017

NeuroImage: Clinical

254

147

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Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifying them manually is costly and challenging for clinicians. In this paper, we propose a novel framework to automatically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs): one is an ensemble of two DeconvNets (Noh et al., 2015), which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net), which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.

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Section: Related Workmentioning

confidence: 99%

Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks

Liang

Bentley

Rueckert

2017

NeuroImage: Clinical

254

147

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“…Recent developments in automatic segmentation by the use of MLAs helped to achieve higher precision [12]. Within the BraTS challenges, the MLAs which yielded the most accurate results included different 2D and 3D convolutional neural networks (CNNs) [13][14][15][16][17], including 3D U-Nets [18,19].…”

Section: Introductionmentioning

confidence: 99%

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

et al. 2021

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Objectives Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods. Methods A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033). Results After the literature search (n = 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86). In addition, a DSC score of 0.83 (95% CI: 0.80–0.87) and 0.82 (95% CI: 0.78–0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed. Conclusion MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set. Key Points • MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86), indicating a good performance. • MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas. • For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.

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“…In [108], 2D deep CNN with fully connected output layers separates HGG and LGG. This approach was further extended by [117] with the two-phase process along with a weighted loss function-initially, the network trains using equiprobable patches that follow actual patch training without the class imbalance problem. In [53], authors had designed 2D InputCascadedCNN, which took the output of TwoPathCNN to train other 2DCNN with the input images.…”

Section: Cnn Methods Classification For Tumor Segmentationmentioning

confidence: 99%

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

Agravat¹,

Raval²

2021

Preprint

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<div>Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results.</div><div>The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.</div>

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Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression

Cited by 31 publications

References 5 publications

Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks

Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

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