Standard
Advancing Kidney, Kidney Tumor, Cyst Segmentation : A Multi-Planner U-Net Approach for the KiTS23 Challenge. / Pandey, Sumit; Toshali, ; Perslev, Mathias; Dam, Erik B.
Kidney and Kidney Tumor Segmentation - MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Proceedings. ed. / Nicholas Heller; Andrew Wood; Christopher Weight; Fabian Isensee; Tim Rädsch; Resha Teipaul; Nikolaos Papanikolopoulos. Springer, 2024. p. 143-148 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14540 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Pandey, S, Toshali,
, Perslev, M & Dam, EB 2024,
Advancing Kidney, Kidney Tumor, Cyst Segmentation: A Multi-Planner U-Net Approach for the KiTS23 Challenge. in N Heller, A Wood, C Weight, F Isensee, T Rädsch, R Teipaul & N Papanikolopoulos (eds),
Kidney and Kidney Tumor Segmentation - MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14540 LNCS, pp. 143-148, 3rd International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2023, which was held in conjunction with 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, Canada,
08/10/2023.
https://doi.org/10.1007/978-3-031-54806-2_20
APA
Pandey, S., Toshali
, Perslev, M., & Dam, E. B. (2024).
Advancing Kidney, Kidney Tumor, Cyst Segmentation: A Multi-Planner U-Net Approach for the KiTS23 Challenge. In N. Heller, A. Wood, C. Weight, F. Isensee, T. Rädsch, R. Teipaul, & N. Papanikolopoulos (Eds.),
Kidney and Kidney Tumor Segmentation - MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Proceedings (pp. 143-148). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14540 LNCS
https://doi.org/10.1007/978-3-031-54806-2_20
Vancouver
Pandey S, Toshali
, Perslev M, Dam EB.
Advancing Kidney, Kidney Tumor, Cyst Segmentation: A Multi-Planner U-Net Approach for the KiTS23 Challenge. In Heller N, Wood A, Weight C, Isensee F, Rädsch T, Teipaul R, Papanikolopoulos N, editors, Kidney and Kidney Tumor Segmentation - MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Proceedings. Springer. 2024. p. 143-148. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14540 LNCS).
https://doi.org/10.1007/978-3-031-54806-2_20
Author
Pandey, Sumit ; Toshali, ; Perslev, Mathias ; Dam, Erik B. / Advancing Kidney, Kidney Tumor, Cyst Segmentation : A Multi-Planner U-Net Approach for the KiTS23 Challenge. Kidney and Kidney Tumor Segmentation - MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Proceedings. editor / Nicholas Heller ; Andrew Wood ; Christopher Weight ; Fabian Isensee ; Tim Rädsch ; Resha Teipaul ; Nikolaos Papanikolopoulos. Springer, 2024. pp. 143-148 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 14540 LNCS).
Bibtex
@inproceedings{115be1568d9a4da6b0eacd48805f55bf,
title = "Advancing Kidney, Kidney Tumor, Cyst Segmentation: A Multi-Planner U-Net Approach for the KiTS23 Challenge",
abstract = "Accurate segmentation of kidney tumors in medical images is crucial for effective treatment planning and patient outcomes prediction. The Kidney and Kidney Tumor Segmentation challenge (KiTS23) serves as a platform for evaluating advanced segmentation methods. In this study, we present our approach utilizing a Multi-Planner U-Net for kidney tumor segmentation. Our method combines the U-Net architecture with multiple image planes to enhance spatial information and improve segmentation accuracy. We employed a 3-fold cross-validation technique on the KiTS23 dataset, evaluating Mean Dice Score, precision, and recall metrics. Results indicate promising performance in segmenting Kidney + Tumor + Cyst and Tumor-only classes, while challenges persist in segmenting Tumor + Cyst cases. Our approach demonstrates potential in kidney tumor segmentation, with room for further refinement to address complex coexisting structures.",
keywords = "kidney tumor, KiTS23 challenge, Multi-Planner U-Net, segmentation",
author = "Sumit Pandey and Toshali and Mathias Perslev and Dam, {Erik B.}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 3rd International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2023, which was held in conjunction with 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 ; Conference date: 08-10-2023 Through 08-10-2023",
year = "2024",
doi = "10.1007/978-3-031-54806-2_20",
language = "English",
isbn = "9783031548055",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "143--148",
editor = "Nicholas Heller and Andrew Wood and Christopher Weight and Fabian Isensee and Tim R{\"a}dsch and Resha Teipaul and Nikolaos Papanikolopoulos",
booktitle = "Kidney and Kidney Tumor Segmentation - MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Proceedings",
address = "Switzerland",
}
RIS
TY - GEN
T1 - Advancing Kidney, Kidney Tumor, Cyst Segmentation
T2 - 3rd International Challenge on Kidney and Kidney Tumor Segmentation, KiTS 2023, which was held in conjunction with 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
AU - Pandey, Sumit
AU - Toshali, null
AU - Perslev, Mathias
AU - Dam, Erik B.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Accurate segmentation of kidney tumors in medical images is crucial for effective treatment planning and patient outcomes prediction. The Kidney and Kidney Tumor Segmentation challenge (KiTS23) serves as a platform for evaluating advanced segmentation methods. In this study, we present our approach utilizing a Multi-Planner U-Net for kidney tumor segmentation. Our method combines the U-Net architecture with multiple image planes to enhance spatial information and improve segmentation accuracy. We employed a 3-fold cross-validation technique on the KiTS23 dataset, evaluating Mean Dice Score, precision, and recall metrics. Results indicate promising performance in segmenting Kidney + Tumor + Cyst and Tumor-only classes, while challenges persist in segmenting Tumor + Cyst cases. Our approach demonstrates potential in kidney tumor segmentation, with room for further refinement to address complex coexisting structures.
AB - Accurate segmentation of kidney tumors in medical images is crucial for effective treatment planning and patient outcomes prediction. The Kidney and Kidney Tumor Segmentation challenge (KiTS23) serves as a platform for evaluating advanced segmentation methods. In this study, we present our approach utilizing a Multi-Planner U-Net for kidney tumor segmentation. Our method combines the U-Net architecture with multiple image planes to enhance spatial information and improve segmentation accuracy. We employed a 3-fold cross-validation technique on the KiTS23 dataset, evaluating Mean Dice Score, precision, and recall metrics. Results indicate promising performance in segmenting Kidney + Tumor + Cyst and Tumor-only classes, while challenges persist in segmenting Tumor + Cyst cases. Our approach demonstrates potential in kidney tumor segmentation, with room for further refinement to address complex coexisting structures.
KW - kidney tumor
KW - KiTS23 challenge
KW - Multi-Planner U-Net
KW - segmentation
U2 - 10.1007/978-3-031-54806-2_20
DO - 10.1007/978-3-031-54806-2_20
M3 - Article in proceedings
AN - SCOPUS:85188749560
SN - 9783031548055
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 143
EP - 148
BT - Kidney and Kidney Tumor Segmentation - MICCAI 2023 Challenge, KiTS 2023, Held in Conjunction with MICCAI 2023, Proceedings
A2 - Heller, Nicholas
A2 - Wood, Andrew
A2 - Weight, Christopher
A2 - Isensee, Fabian
A2 - Rädsch, Tim
A2 - Teipaul, Resha
A2 - Papanikolopoulos, Nikolaos
PB - Springer
Y2 - 8 October 2023 through 8 October 2023
ER -
