Acta Universitatis Sapientiae Informatica · 2022 · 13 citations · 12 references
ComputingComputer ScienceOncologyComputer VisionMachine LearningNeuro-oncologyCancer DetectionDeep LearningImage SegmentationMedical Image AnalysisNeural NetworksTumor DetectionBackground RemovalConvolutional Neural Network DetectionConvolutional Neural Network SegmentationConvolutional Neural NetworksDiceLesion SegmentationPost-processingTumor Segmentation
Abstract In this paper we propose to create an end-to-end brain tumor segmentation system that applies three variants of the well-known U-Net convolutional neural networks. In our results we obtain and analyse the detection performances of U-Net, VGG16-UNet and ResNet-UNet on the BraTS2020 training dataset. Further, we inspect the behavior of the ensemble model obtained as the weighted response of the three CNN models. We introduce essential preprocessing and post-processing steps so as to improve the detection performances. The original images were corrected and the different intensity ranges were transformed into the 8-bit grayscale domain to uniformize the tissue intensities, while preserving the original histogram shapes. For post-processing we apply region connectedness onto the whole tumor and conversion of background pixels into necrosis inside the whole tumor. As a result, we present the Dice scores of our system obtained for WT (whole tumor), TC (tumor core) and ET (enhanced tumor) on the BraTS2020 training dataset.
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Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Sérgio Pereira, Adriano Pinto, Victor Alves et al. · IEEE Transactions on Medical Imaging · 2016 · 2.7K citations
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Xiaomei Zhao, Yihong Wu, Guidong Song et al. · Medical Image Analysis · 2017 · 811 citations · Full text