Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction

Russo, Carlo; Liu, Sidong; Ieva, Antonio Di

doi:10.1007/978-3-030-72084-1_27

Cited by 13 publications

(16 citation statements)

References 20 publications

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“…A larger sample size with more severe and converter cases in the datasets would help train more accurate and robust models as well as produce reliable performance estimates. Other techniques, such as synthetic minority oversampling [29], spherical coordinates transformation [49], and generative adversarial network [50], will be investigated in further study.…”

Section: Discussionmentioning

confidence: 99%

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

et al. 2021

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Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.

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Section: Discussionmentioning

confidence: 99%

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

et al. 2021

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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%

“…Russo et al [45] proposed spherical coordinate transformation as a pre-processing stage to improve segmentation results. Pre-training weights for future training stages were achievable due to the utilisation of spherical transformation in the first cascade pass.…”

Section: Spherical Coordinate Transformationmentioning

confidence: 99%

“…In contrast to conventional Cartesian space imagery and volumes, Russo et al [45] presented a new technique for feeding DCNN with spherical dimension converted input data to improve feature learning. The DCNN used as a baseline for the proposed approach was developed from the work of Myronenko [56], built on a variational autoencoder (VAE) UNet with modified input shape and loss function depending on the kind of transformation employed in the pre-processing phase.…”

Section: Cascaded Networkmentioning

confidence: 99%

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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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“…The BraTS 2020 test dataset was used for testing the model that achieved dice scores of 81%, 91%, and 95% for the enhanced tumor, whole tumor, and tumor core. Carlo Russo [37] used spherical space transformed input data to extract better features than standard feature extraction methods. The spherical coordinate transformation was used as pre-processing to improve the accuracy for brain tumor segmentation on the BraTS 2020 dataset.…”

Section: Using Dropout Regularizationmentioning

confidence: 99%

RMU-Net: A Novel Residual Mobile U-Net Model for Brain Tumor Segmentation from MR Images

et al. 2021

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The most aggressive form of brain tumor is gliomas, which leads to concise life when high grade. The early detection of glioma is important to save the life of patients. MRI is a commonly used approach for brain tumors evaluation. However, the massive amount of data provided by MRI prevents manual segmentation in a reasonable time, restricting the use of accurate quantitative measurements in clinical practice. An automatic and reliable method is required that can segment tumors accurately. To achieve end-to-end brain tumor segmentation, a hybrid deep learning model RMU-Net is proposed. The architecture of MobileNetV2 is modified by adding residual blocks to learn in-depth features. This modified Mobile Net V2 is used as an encoder in the proposed network, and upsampling layers of U-Net are used as the decoder part. The proposed model has been validated on BraTS 2020, BraTS 2019, and BraTS 2018 datasets. The RMU-Net achieved the dice coefficient scores for WT, TC, and ET of 91.35%, 88.13%, and 83.26% on the BraTS 2020 dataset, 91.76%, 91.23%, and 83.19% on the BraTS 2019 dataset, and 90.80%, 86.75%, and 79.36% on the BraTS 2018 dataset, respectively. The performance of the proposed method outperforms with less computational cost and time as compared to previous methods.

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Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction

Cited by 13 publications

References 20 publications

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT

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

RMU-Net: A Novel Residual Mobile U-Net Model for Brain Tumor Segmentation from MR Images

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