Kidney and Kidney Tumor Segmentation (eBook, PDF)

MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings
Redaktion: Heller, Nicholas; Weight, Christopher; Papanikolopoulos, Nikolaos; Teipaul, Resha; Rädsch, Tim; Isensee, Fabian; Wood, Andrew

• Format: PDF

Produktbeschreibung

This book constitutes the Third International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2023, which was held in conjunction with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023. The challenge took place in Vancouver, BC, Canada, on October 8, 2023.

The 22 contributions presented in this book were carefully reviewed and selected from 29 submissions.

This challenge aims to develop the best system for automatic semantic segmentation of kidneys, renal tumors and renal cysts.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 164
• Erscheinungstermin: 6. März 2024
• Englisch
• ISBN-13: 9783031548062
• Artikelnr.: 70129187

Inhaltsangabe

Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023.- Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge.- Dynamic resolution network for kidney tumor segmentation.- Analyzing domain shift when using additional data for the MICCAI KiTS23 Challenge.- A Hybrid Network based on nnU-net and Swin Transformer for Kidney Tumor Segmentation.- Leveraging Uncertainty Estimation for Segmentation of Kidney, Kidney Tumor and Kidney Cysts.- An Ensemble of 2.5D ResUnet Based Models for Segmentation of Kidney and Masses.- Using Uncertainty Information for Kidney Tumor Segmentation.- Two-Stage Segmentation and Ensemble Modeling: Kidney Tumor Analysis in CT Images.- GSCA-Net: A global spatial channel attention network for kidney, tumor and cyst segmentation.- Genetic Algorithm enhanced nnU-Net for the MICCAI KiTS23 Challenge.- Two-Stage Segmentation Framework with Parallel Decoders for the Kidney and Kidney Tumor Segmentation.- 3d U-Net with ROI Segmentation of Kidneys and Masses in CT Scans.- Deep Learning-Based Hierarchical Delineation of Kidneys, Tumors, and Cysts in CT Images.- Cascade UNets for Kidney and Kidney Tumor Segmentation.- Cascaded nnU-Net for Kidney and Kidney Tumor Segmentation.- A Deep Learning Approach for the Segmentation of Kidney, Tumor and Cyst in Computed Tomography Scans.- Recursive learning reinforced by redefining the train and validation volumes of an Encoder-Decoder segmentation model.- Attention U-net for Kidney and Masses.- Advancing Kidney, Kidney Tumor, Cyst Segmentation: A Multi-Planner U-Net Approach for the KiTS23 Challenge.- 3D Segmentation of Kidneys, Kidney Tumors and Cysts on CT Images - KiTS23 Challenge.- Kidney and Kidney Tumor Segmentation via Transfer Learning.