Kidney and kidney tumor segmentation : MICCAI 2021 Challenge, KiTS 2021, held in conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, proceedings / / edited by Nicholas Heller, [and five others]

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (173 pages)
• Disciplina: 616.99461
• Soggetto topico: Electronic data processing; Punched card systems
• Persona (resp. second.): HellerNicholas (Doctoral student)
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Automated Kidney Tumor Segmentation with Convolution and Transformer Network -- 1 Introduction -- 2 Related Work -- 2.1 Medical Image Segmentation -- 2.2 Self-attention Mechanism -- 3 Methods -- 3.1 Network Architecture -- 3.2 Loss Function -- 3.3 Pre- and post- processing -- 3.4 Implementation Details -- 4 Results -- 4.1 Dataset -- 4.2 Metrics -- 4.3 Results on KITS21 Training Set -- 4.4 Results on KITS21 Test Set -- 5 Discussion and Conclusion -- References -- Extraction of Kidney Anatomy Based on a 3D U-ResNet with Overlap-Tile Strategy -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 2.4 Postprocessing -- 3 Results -- 4 Discussion and Conclusion -- References -- Modified nnU-Net for the MICCAI KiTS21 Challenge -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- 2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- Automated Machine Learning Algorithm for Kidney, Kidney Tumor, Kidney Cyst Segmentation in Computed Tomography Scans -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Network Architecture -- 2.4 Network Training -- 3 Results -- 4 Discussion and Conclusion -- References -- Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net -- 1 Introduction -- 2 Methods -- 2.1 Network Architecture -- 2.2 Segmentation from Low-Resolution CT -- 2.3 Fine Segmentation of Kidney -- 2.4 Segmentation of Tumor and Cysts -- 2.5 Training Protocols -- 3 Results. ; 4 Discussion and Conclusion -- References -- Less is More: Contrast Attention Assisted U-Net for Kidney, Tumor and Cyst Segmentations -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Network Architecture -- 3 Results -- 4 Discussion and Conclusion -- References -- A Coarse-to-Fine Framework for the 2021 Kidney and Kidney Tumor Segmentation Challenge -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 3.1 Metric -- 3.2 Results and Discussions -- 4 Conclusion -- References -- Kidney and Kidney Tumor Segmentation Using a Two-Stage Cascade Framework -- 1 Introduction -- 2 Methods -- 2.1 Kidney-Net -- 2.2 Masses-Net -- 2.3 Loss Function -- 3 Experiment -- 3.1 Datasets -- 3.2 Pre-processing and Post-processing -- 3.3 Training and Implementation Details -- 3.4 Metrics -- 4 Results and Discussion -- 5 Conclusion -- References -- Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Images -- 1 Introduction -- 2 Method -- 2.1 Architecture -- 2.2 Squeeze-and-Excitation Module -- 2.3 Deep Supervision -- 2.4 Loss Function -- 3 Experiments -- 3.1 Datasets -- 3.2 Metrics -- 3.3 Pre- and Post-processing -- 3.4 Implementation Details -- 4 Result -- 5 Discussion and Conclusion -- References -- A Two-Stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Kidney Localization Network -- 2.2 Multi-decoding Segmentation Network -- 2.3 Global Context Fusion Block -- 2.4 Regional Constraint Loss Function -- 3 Experimental Results -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusion -- References -- Mixup Augmentation for Kidney and Kidney Tumor Segmentation -- 1 Introduction -- 2 Methods. ; 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion -- References -- Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans -- 1 Introduction -- 2 nnU-Net Determined Details -- 2.1 3D U-Net Network Architecture -- 2.2 3D U-Net Cascade Network Architecture -- 2.3 Preprocessing -- 2.4 Training Details -- 3 Method -- 3.1 Training and Validation Data -- 3.2 Pretraining -- 3.3 Annotations -- 3.4 Regularized Loss -- 3.5 Postprocessing -- 3.6 Final Submission -- 4 Results -- 4.1 Single-Stage, High-Resolution 3D U-Net -- 4.2 3D U-Net Cascade -- 4.3 Model Ensemble -- 4.4 Postprocessing -- 4.5 Test Set Results -- 5 Discussion and Conclusions -- References -- Contrast-Enhanced CT Renal Tumor Segmentation -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- A Cascaded 3D Segmentation Model for Renal Enhanced CT Images -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT -- 1 Introduction -- 2 Materials and Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Baseline 3D U-Net -- 2.4 Cognizant Sampling Leveraging Clinical Characteristics -- 2.5 Statistical Evaluation -- 3 Results -- 4 Discussion and Conclusion -- References. ; A Coarse-to-Fine 3D U-Net Network for Semantic Segmentation of Kidney CT Scans -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Data Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- 3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Network Architecture -- 2.4 Loss Function -- 2.5 Optimization Strategy -- 2.6 Validation -- 2.7 Post-processing -- 3 Results -- 4 Discussion and Conclusion -- References -- Kidney and Kidney Tumor Segmentation Using Spatial and Channel Attention Enhanced U-Net -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Data Augmentations -- 2.4 Proposed Method -- 2.5 Residual U-Net for Comparison -- 2.6 Implementation and Training -- 2.7 Inference Procedure -- 3 Results -- 4 Conclusion -- References -- Transfer Learning for KiTS21 Challenge -- 1 Introduction -- 2 Methods -- 2.1 Training and Validation Data -- 2.2 Preprocessing -- 2.3 Proposed Method -- 3 Results -- 4 Discussion and Conclusion -- References -- Author Index.
• Titolo autorizzato: Kidney and kidney tumor segmentation
• ISBN: 3-030-98385-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996464542303316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Computer Science