Brain tumor segmentation using a hybrid multi resolution U-Net with residual dual attention and deep supervision on MR images
Authors:
- Subin SahayamSSSubin Sahayam
- Rahul NenavathRNRahul Nenavath
- Umarani JayaramanUJUmarani Jayaraman
- Surya PrakashSPSurya Prakash
- 27 June 2022
- journal article
- research article
- Published by Elsevier in Biomedical Signal Processing and Control
- Vol. 78
Abstract
No abstract availableKeywords
This publication has 15 references indexed in Scilit:
- Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic featuresScientific Data, 2017
- Brain tumor segmentation with Deep Neural NetworksMedical Image Analysis, 2017
- The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summaryActa Neuropathologica, 2016
- Review of medical image quality assessmentBiomedical Signal Processing and Control, 2016
- Brain Tumor Segmentation Using Convolutional Neural Networks in MRI ImagesIEEE Transactions on Medical Imaging, 2016
- The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)IEEE Transactions on Medical Imaging, 2015
- Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain ImagesNeuroinformatics, 2015
- The Design of SimpleITKFrontiers in Neuroinformatics, 2013
- Snakes: Active contour modelsInternational Journal of Computer Vision, 1988
- A Threshold Selection Method from Gray-Level HistogramsIEEE Transactions on Systems, Man, and Cybernetics, 1979