05063nam a22005895i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001600172050001100188072001600199072002300215072001500238245032600253250001800579264006900597300006300666336002600729337002600755338003600781347002400817490005900841505184000900520055502740650004903295650002103344650002603365650002903391650007103420650005103491650002903542700008603571700008203657700008503739700008303824700008403907700009603991700008904087710003404176773002604210776003604236776003604272830005904308856004604367912001404413912001504427912001404442999001704456978-3-031-54806-2DE-He21320251030111502.0cr nn 008mamaa240306s2024    sz |    s    |||| 0|eng d  a97830315480629978-3-031-54806-27 a10.1007/978-3-031-54806-22doi 4aTA1501-1820 4aTA1634 7aUYT2bicssc 7aCOM0160002bisacsh 7aUYT2thema10aKidney and Kidney Tumor Segmentationh[electronic resource] :bMICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings /cedited by Nicholas Heller, Andrew Wood, Fabian Isensee, Tim Rädsch, Resha Teipaul, Nikolaos Papanikolopoulos, Christopher Weight.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aX, 164 p. 76 illus., 73 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aLecture Notes in Computer Science,x1611-3349 ;v145400 aAutomated 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.  aThis 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. 0aImage processingxDigital techniques.964228 0aComputer vision. 0aApplication software. 0aMachine learning.95505914aComputer Imaging, Vision, Pattern Recognition and Graphics.96555024aComputer and Information Systems Applications.24aMachine Learning.9550591 aHeller, Nicholas.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9725011 aWood, Andrew.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9725021 aIsensee, Fabian.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9725031 aRädsch, Tim.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9725041 aTeipaul, Resha.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9725051 aPapanikolopoulos, Nikolaos.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9725061 aWeight, Christopher.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9725072 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303154805508iPrinted edition:z9783031548079 0aLecture Notes in Computer Science,x1611-3349 ;v1454040uhttps://doi.org/10.1007/978-3-031-54806-2  aZDB-2-SCS  aZDB-2-SXCS  aZDB-2-LNC  c58133d58133