Brain Tumor Segmentation and Survival Prediction Using Patch Based Modified 3D U-Net

Parmar, Bhavesh; Parikh, Mehul C.

doi:10.1007/978-3-030-72087-2_35

Cited by 8 publications

(20 citation statements)

References 16 publications

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“…As shown in Fig. 7, the authors [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46] employed the following pre-processing methods to account for intensity inhomogeneity throughout the dataset.…”

Section: Pre-processingmentioning

confidence: 99%

“…6 The generic workflow for brain tumour segmentation and overall survival prediction Fig. 7 Types of pre-processing used in BraTS 2020 SP techniques feeding the MRIs as input for model training, González et al [29], Parmar et al [30], and Carmo et al [31] normalised each MRI sequence in the range [0, 1].…”

Section: Min-max Normalizationmentioning

confidence: 99%

“…It deals with outliers but does not generate normalised data on the same scale. Researchers used z score normalization to set each MRI volume's mean and unit standard deviation before these volumes were fed into training models [29][30][31][32][33][34][35][36][37][38][39][40][41][42].…”

Section: Z-score Normalizationmentioning

confidence: 99%

“…Medical image analysis, especially tumour delineation, is affected by this problem [48]. Several data augmentation techniques were used by researchers to increase the number of training samples which included random axis mirror flips [29,30,[33][34][35][36][37][38]49], intensity scaling and intensity shifting [31,34,37], isotropic scaling, per-channel gamma corrections [35], rotations [35-37, 43, 49], random scaling [38], elastic transformations, and zoom-in and zoom-out [49]. There are many ways of augmenting MRI scans, as illustrated in Fig.…”

Section: Data Augmentationmentioning

confidence: 99%

“…Parmar et al [30] showed that an increase in batch size might lead to smaller patches with less contextual information. Contrarily, a bigger patch size may provide more contextual information, resulting in smaller batch size, raising the stochastic gradient variance, and reducing the amount of optimization achieved.…”

Section: Single Networkmentioning

confidence: 99%

See 4 more Smart Citations

State-of-the-art techniques using pre-operative brain MRI scans for survival prediction of glioblastoma multiforme patients and future research directions

Kaur

Rana

Arora

2022

Clin Transl Imaging

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Objective Glioblastoma multiforme (GBM) is a grade IV brain tumour with very low life expectancy. Physicians and oncologists urgently require automated techniques in clinics for brain tumour segmentation (BTS) and survival prediction (SP) of GBM patients to perform precise surgery followed by chemotherapy treatment. Methods This study aims at examining the recent methodologies developed using automated learning and radiomics to automate the process of SP. Automated techniques use pre-operative raw magnetic resonance imaging (MRI) scans and clinical data related to GBM patients. All SP methods submitted for the multimodal brain tumour segmentation (BraTS) challenge are examined to extract the generic workflow for SP. ResultsThe maximum accuracies achieved by 21 state-of-the-art different SP techniques reviewed in this study are 65.5 and 61.7% using the validation and testing subsets of the BraTS dataset, respectively. The comparisons based on segmentation architectures, SP models, training parameters and hardware configurations have been made. ConclusionThe limited accuracies achieved in the literature led us to review the various automated methodologies and evaluation metrics to find out the research gaps and other findings related to the survival prognosis of GBM patients so that these accuracies can be improved in future. Finally, the paper provides the most promising future research directions to improve the performance of automated SP techniques and increase their clinical relevance.

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