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Record Nr. |
UNINA9910556885903321 |
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Titolo |
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, Fabian Isensee, Darya Trofimova, Resha Tejpaul, Nikolaos Papanikolopoulos, Christopher Weight |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
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ISBN |
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Edizione |
[1st ed. 2022.] |
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Descrizione fisica |
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1 online resource (173 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 13168 |
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Disciplina |
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Soggetti |
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Image processing - Digital techniques |
Computer vision |
Application software |
Machine learning |
Computer Imaging, Vision, Pattern Recognition and Graphics |
Computer and Information Systems Applications |
Machine Learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Automated kidney tumor segmentation with convolution and transformer network -- Extraction of Kidney Anatomy based on a 3D U-ResNet with Overlap-Tile Strategy -- Modified nnU-Net for the MICCAI KiTS21 Challenge -- 2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst -- Automated Machine Learning algorithm for Kidney, Kidney tumor, Kidney Cyst segmentation in Computed Tomography Scans -- Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net -- Less is More: Contrast Attention assisted U-Net for Kidney, Tumor and Cyst Segmentations -- A Coarse-to-fine Framework for The 2021 Kidney and Kidney Tumor Segmentation Challenge -- Kidney and kidney tumor segmentation using a two-stage cascade framework -- Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor |
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Segmentation in CT images -- A Two-stage Cascaded Deep Neural Network with Multi-decoding Paths for Kidney Tumor Segmentation -- Mixup Augmentation for Kidney and Kidney TumorSegmentation -- Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge -- An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans -- Contrast-Enhanced CT Renal Tumor Segmentation -- A Cascaded 3D Segmentation Model for Renal Enhanced CT Images -- Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on CT -- A Coarse-to-Fine 3D U-Net Network for Semantic Segmentation of Kidney CT Scans -- 3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT -- Kidney and Kidney Tumor Segmentation using Spatial and Channel attention enhanced U-Net Transfer Learning for KiTS21 Challenge. |
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Sommario/riassunto |
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This book constitutes the Second International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021, which was held in conjunction with the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021. The challenge took place virtually on September 27, 2021, due to the COVID-19 pandemic. The 21 contributions presented were carefully reviewed and selected from 29 submissions. This challenge aims to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. |
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