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024 7 _a10.1007/978-3-031-54806-2
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245 1 0 _aKidney and Kidney Tumor Segmentation
_h[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.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aX, 164 p. 76 illus., 73 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v14540
505 0 _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.
520 _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.
650 0 _aImage processing
_xDigital techniques.
650 0 _aComputer vision.
650 0 _aApplication software.
650 0 _aMachine learning.
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aMachine Learning.
700 1 _aHeller, Nicholas.
_eeditor.
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_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aWood, Andrew.
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700 1 _aIsensee, Fabian.
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700 1 _aRädsch, Tim.
_eeditor.
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700 1 _aTeipaul, Resha.
_eeditor.
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700 1 _aPapanikolopoulos, Nikolaos.
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700 1 _aWeight, Christopher.
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710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783031548079
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
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856 4 0 _uhttps://doi.org/10.1007/978-3-031-54806-2
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