Optimized U-Net for Brain Tumor Segmentation
We propose an optimized U-Net architecture for a brain tumor segmentation task in the BraTS21 challenge. To find the optimal model architecture and the learning schedule, we have run an extensive ablation study to test: deep supervision loss, Focal loss, decoder attention, drop block, and residual connections. Additionally, we have searched for the optimal depth of the U-Net encoder, number of convolutional channels and post-processing strategy. Our method won the validation phase and took third place in the test phase. We have open-sourced the code to reproduce our BraTS21 submission at the NVIDIA Deep Learning Examples GitHub Repository.
Code (2)
Tasks
Brain Tumor SegmentationDecoderTumor SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors
Accurate segmentation of brain tumors from 3D multimodal MRI is vital for diagnosis and treatment planning across diverse brain tumors. This paper addresses the challenges posed by the BraTS 2023, presenting a unified tr…
Brain Tumor SegmentationImage SegmentationMedical Image SegmentationSegmentation+3Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool…
Brain Tumor SegmentationDiagnosticImage SegmentationSegmentation+2CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification
In today's world of health care, brain tumor detection has become common. However, the manual brain tumor classification approach is time-consuming. So Deep Convolutional Neural Network (DCNN) is used by many researchers…
Brain Tumor ClassificationBrain Tumor SegmentationClassificationTopological Data Analysis+2QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors
Non-invasive techniques such as magnetic resonance imaging (MRI) are widely employed in brain tumor diagnostics. However, manual segmentation of brain tumors from 3D MRI volumes is a time-consuming task that requires tra…
Brain Tumor SegmentationDiagnosticSegmentationTumor SegmentationA Review on End-To-End Methods for Brain Tumor Segmentation and Overall Survival Prediction
Brain tumor segmentation intends to delineate tumor tissues from healthy brain tissues. The tumor tissues include necrosis, peritumoral edema, and active tumor. In contrast, healthy brain tissues include white matter, gr…
Brain Tumor SegmentationSegmentationSurvival PredictionTumor Segmentation