Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net

Yi Lv; Junchen Wang

doi:10.1007/978-3-030-98385-7_6

Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net

Yi Lv
, Junchen Wang^*

^*Corresponding author for this work

School of Mechanical Engineering and Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Medical image processing plays an increasingly important role in clinical diagnosis and treatment. Using the results of kidney CT image segmentation for three-dimensional reconstruction is an intuitive and accurate method for diagnosis. In this paper, we propose a three-step automatic segmentation method for kidney, tumors and cysts, including roughly segmenting the kidney and tumor from low-resolution CT, locating each kidney and fine segmenting the kidney, and finally extracting the tumor and cyst from the segmented kidney. The results show that the average dice of our method for kidney, tumor and cysts is about 0.93, 0.57, 0.73.

Original language	English
Title of host publication	Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Proceedings
Editors	Nicholas Heller, Fabian Isensee, Darya Trofimova, Resha Tejpaul, Nikolaos Papanikolopoulos, Christopher Weight
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	40-45
Number of pages	6
ISBN (Print)	9783030983840
DOIs	https://doi.org/10.1007/978-3-030-98385-7_6
State	Published - 2022
Event	2nd International challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: 27 Sep 2021 → 27 Sep 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13168 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	27/09/21 → 27/09/21

Keywords

Deep learning
Medical image segmentation
Neural network

Access to Document

10.1007/978-3-030-98385-7_6

Cite this

Lv, Y., & Wang, J. (2022). Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net. In N. Heller, F. Isensee, D. Trofimova, R. Tejpaul, N. Papanikolopoulos, & C. Weight (Eds.), Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Proceedings (pp. 40-45). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13168 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-98385-7_6

Lv, Yi ; Wang, Junchen. / Three Uses of One Neural Network : Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net. Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Proceedings. editor / Nicholas Heller ; Fabian Isensee ; Darya Trofimova ; Resha Tejpaul ; Nikolaos Papanikolopoulos ; Christopher Weight. Springer Science and Business Media Deutschland GmbH, 2022. pp. 40-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{4ff1630204624f37b97201ba2b0c0545,

title = "Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net",

abstract = "Medical image processing plays an increasingly important role in clinical diagnosis and treatment. Using the results of kidney CT image segmentation for three-dimensional reconstruction is an intuitive and accurate method for diagnosis. In this paper, we propose a three-step automatic segmentation method for kidney, tumors and cysts, including roughly segmenting the kidney and tumor from low-resolution CT, locating each kidney and fine segmenting the kidney, and finally extracting the tumor and cyst from the segmented kidney. The results show that the average dice of our method for kidney, tumor and cysts is about 0.93, 0.57, 0.73.",

keywords = "Deep learning, Medical image segmentation, Neural network",

author = "Yi Lv and Junchen Wang",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 2nd International challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 27-09-2021",

year = "2022",

doi = "10.1007/978-3-030-98385-7\_6",

language = "英语",

isbn = "9783030983840",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "40--45",

editor = "Nicholas Heller and Fabian Isensee and Darya Trofimova and Resha Tejpaul and Nikolaos Papanikolopoulos and Christopher Weight",

booktitle = "Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Proceedings",

address = "德国",

}

Lv, Y & Wang, J 2022, Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net. in N Heller, F Isensee, D Trofimova, R Tejpaul, N Papanikolopoulos & C Weight (eds), Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13168 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 40-45, 2nd International challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, Virtual, Online, 27/09/21. https://doi.org/10.1007/978-3-030-98385-7_6

Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net. / Lv, Yi; Wang, Junchen.
Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Proceedings. ed. / Nicholas Heller; Fabian Isensee; Darya Trofimova; Resha Tejpaul; Nikolaos Papanikolopoulos; Christopher Weight. Springer Science and Business Media Deutschland GmbH, 2022. p. 40-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13168 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Three Uses of One Neural Network

T2 - 2nd International challenge on Kidney and Kidney Tumor Segmentation, KiTS 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021

AU - Lv, Yi

AU - Wang, Junchen

PY - 2022

Y1 - 2022

N2 - Medical image processing plays an increasingly important role in clinical diagnosis and treatment. Using the results of kidney CT image segmentation for three-dimensional reconstruction is an intuitive and accurate method for diagnosis. In this paper, we propose a three-step automatic segmentation method for kidney, tumors and cysts, including roughly segmenting the kidney and tumor from low-resolution CT, locating each kidney and fine segmenting the kidney, and finally extracting the tumor and cyst from the segmented kidney. The results show that the average dice of our method for kidney, tumor and cysts is about 0.93, 0.57, 0.73.

AB - Medical image processing plays an increasingly important role in clinical diagnosis and treatment. Using the results of kidney CT image segmentation for three-dimensional reconstruction is an intuitive and accurate method for diagnosis. In this paper, we propose a three-step automatic segmentation method for kidney, tumors and cysts, including roughly segmenting the kidney and tumor from low-resolution CT, locating each kidney and fine segmenting the kidney, and finally extracting the tumor and cyst from the segmented kidney. The results show that the average dice of our method for kidney, tumor and cysts is about 0.93, 0.57, 0.73.

KW - Deep learning

KW - Medical image segmentation

KW - Neural network

UR - https://www.scopus.com/pages/publications/85127646138

U2 - 10.1007/978-3-030-98385-7_6

DO - 10.1007/978-3-030-98385-7_6

M3 - 会议稿件

AN - SCOPUS:85127646138

SN - 9783030983840

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 40

EP - 45

BT - Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Proceedings

A2 - Heller, Nicholas

A2 - Isensee, Fabian

A2 - Trofimova, Darya

A2 - Tejpaul, Resha

A2 - Papanikolopoulos, Nikolaos

A2 - Weight, Christopher

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 27 September 2021 through 27 September 2021

ER -

Lv Y, Wang J. Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net. In Heller N, Isensee F, Trofimova D, Tejpaul R, Papanikolopoulos N, Weight C, editors, Kidney and Kidney Tumor Segmentation - MICCAI 2021 Challenge, KiTS 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 40-45. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-98385-7_6

Three Uses of One Neural Network: Automatic Segmentation of Kidney Tumor and Cysts Based on 3D U-Net

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this