PMID- 37859296
OWN - NLM
STAT- MEDLINE
DCOM- 20231023
LR  - 20231023
IS  - 1879-0534 (Electronic)
IS  - 0010-4825 (Linking)
VI  - 150
DP  - 2022 Nov
TI  - Segmentation of kidney mass using AgDenseU-Net 2.5D model.
PG  - 106223
LID - S0010-4825(22)00931-3 [pii]
LID - 10.1016/j.compbiomed.2022.106223 [doi]
AB  - The Kidney and Kidney Tumor Segmentation Challenge 2021 (KiTS21) released a 
      kidney CT dataset with 300 patients. Unlike KiTS19, KiTS21 provided a cyst 
      category. Therefore, the segmentation of kidneys, tumors, and cysts will be able 
      to assess the complexity and aggressiveness of kidney mass. Deep learning models 
      can save medical resources, but 3D models still have some disadvantages, such as 
      the high cost of computing resources. This paper proposes a scheme that saves 
      computing resources and achieves the segmentation of kidney mass in two steps. 
      First, we preprocess the kidney volume data using the automatic down-sampling 
      method of 3D images, reducing the volume while preserving the feature 
      information. Second, we finely segment kidneys, tumors, and cysts using the 
      AgDenseU-Net (Attention gate DenseU-Net) 2.5D model. KiTS21 proposed using 
      Hierarchical Evaluation Classes (HECs) to compute a metric for the superset: the 
      HEC of kidney considers kidneys, tumors, and cysts as the foreground to compute 
      segmentation performance; the HEC of kidney mass considers both tumor and cyst as 
      the foreground classes; the HEC of tumor considers tumor as the foreground only. 
      For KiTS21, our model achieved a dice score of 0.971 for the kidney, 0.883 for 
      the mass, and 0.815 for the tumor. In addition, we also tested segmentation 
      results without HECs, and our model achieved a dice score of 0.950 for the 
      kidney, 0.878 for the tumor, and 0.746 for the cyst. The results demonstrate that 
      the method proposed in this paper can be used as a reference for kidney tumor 
      segmentation.
CI  - Copyright (c) 2022 Elsevier Ltd. All rights reserved.
FAU - Sun, Peng
AU  - Sun P
AD  - School of Electronic Engineering and Automation, Guilin University of Electronic 
      Technology, Guilin, Guangxi, 541004, China.
FAU - Mo, Zengnan
AU  - Mo Z
AD  - Center for Genomic and Personalized Medicine, Guangxi Medical University, 
      Nanning, Guangxi, 530021, China.
FAU - Hu, Fangrong
AU  - Hu F
AD  - School of Electronic Engineering and Automation, Guilin University of Electronic 
      Technology, Guilin, Guangxi, 541004, China.
FAU - Song, Xin
AU  - Song X
AD  - School of Electronic Engineering and Automation, Guilin University of Electronic 
      Technology, Guilin, Guangxi, 541004, China.
FAU - Mo, Taiping
AU  - Mo T
AD  - School of Electronic Engineering and Automation, Guilin University of Electronic 
      Technology, Guilin, Guangxi, 541004, China.
FAU - Yu, Bonan
AU  - Yu B
AD  - School of Architecture and Transportation Engineering, Guilin University of 
      Electronic Technology, Guilin, Guangxi, 541004, China.
FAU - Zhang, Yewei
AU  - Zhang Y
AD  - Hepatopancreatobiliary Center, The Second Affiliated Hospital of Nanjing Medical 
      University, Nanjing, China. Electronic address: zhangyewei@njmu.edu.cn.
FAU - Chen, Zhencheng
AU  - Chen Z
AD  - School of Electronic Engineering and Automation, Guilin University of Electronic 
      Technology, Guilin, Guangxi, 541004, China. Electronic address: 
      zhenchchen@163.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20221018
PL  - United States
TA  - Comput Biol Med
JT  - Computers in biology and medicine
JID - 1250250
SB  - IM
MH  - Humans
MH  - *Kidney Neoplasms/diagnostic imaging
MH  - *Cysts
MH  - Kidney/diagnostic imaging
MH  - Image Processing, Computer-Assisted
OTO - NOTNLM
OT  - 2.5D model
OT  - AgDenseU-Net
OT  - Automatic down-sampling of 3D images
OT  - KiTS21
OT  - Kidney tumor segmentation
OT  - Medical image segmentation
COIS- Declaration of competing interest The authors declare no conflict of interest.
EDAT- 2023/10/20 06:42
MHDA- 2023/10/23 01:18
CRDT- 2023/10/20 01:07
PHST- 2022/05/14 00:00 [received]
PHST- 2022/10/07 00:00 [revised]
PHST- 2022/10/15 00:00 [accepted]
PHST- 2023/10/23 01:18 [medline]
PHST- 2023/10/20 06:42 [pubmed]
PHST- 2023/10/20 01:07 [entrez]
AID - S0010-4825(22)00931-3 [pii]
AID - 10.1016/j.compbiomed.2022.106223 [doi]
PST - ppublish
SO  - Comput Biol Med. 2022 Nov;150:106223. doi: 10.1016/j.compbiomed.2022.106223. Epub 
      2022 Oct 18.