PMID- 33708918
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220421
IS  - 2305-5839 (Print)
IS  - 2305-5847 (Electronic)
IS  - 2305-5839 (Linking)
VI  - 9
IP  - 4
DP  - 2021 Feb
TI  - Automatic segmentation of pulmonary lobes on low-dose computed tomography using 
      deep learning.
PG  - 291
LID - 10.21037/atm-20-5060 [doi]
LID - 291
AB  - BACKGROUND: To develop and validate a fully automated deep learning-based 
      segmentation algorithm to segment pulmonary lobe on low-dose computed tomography 
      (LDCT) images. METHODS: This study presents an automatic segmentation of 
      pulmonary lobes using a fully convolutional neural network named dense V-network 
      (DenseVNet) on lung cancer screening LDCT images. A total of 160 LDCT cases for 
      lung cancer screening (100 in the training set, 10 in the validation set, and 50 
      in the test set) was included in this study. Specifically, the template of 
      pulmonary lobes (the right lung consists of three lobes, and the left lung 
      consists of two lobes) were obtained from pixel-level annotations by 
      semiautomatic segmentation platform. Then, the model was trained under the 
      supervision of the LDCT training set. Finally, the trained model was used to 
      segment the LDCT in the test set. Dice coefficient, Jaccard coefficient, and 
      Hausdorff distance were adopted as evaluation metrics to verify the performance 
      of our segmentation model. RESULTS: In this study, the model achieved the 
      accurate segmentation of each pulmonary lobe in seconds without the intervention 
      of researchers. The testing set consisted 50 LDCT cases were used to evaluate the 
      performance of the segmentation model. The all-lobes Dice coefficient of the test 
      set was 0.944, the Jaccard coefficient was 0.896, and the Hausdorff distance was 
      92.908 mm. CONCLUSIONS: The segmentation model based on LDCT can automatically 
      and robustly and efficiently segment pulmonary lobes. It will provide effective 
      location information and contour constraints for pulmonary nodule detection on 
      LDCT images for lung cancer screening, which may have potential clinical 
      application.
CI  - 2021 Annals of Translational Medicine. All rights reserved.
FAU - Zhang, Zewei
AU  - Zhang Z
AD  - PET-CT Center, National Cancer Center/National Clinical Research Center for 
      Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union 
      Medical College, Beijing, China.
FAU - Ren, Jialiang
AU  - Ren J
AD  - GE Healthcare China, Beijing, China.
FAU - Tao, Xiuli
AU  - Tao X
AD  - PET-CT Center, National Cancer Center/National Clinical Research Center for 
      Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union 
      Medical College, Beijing, China.
FAU - Tang, Wei
AU  - Tang W
AD  - Department of Diagnostic Radiology, National Cancer Center/National Clinical 
      Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences 
      and Peking Union Medical College, Beijing, China.
FAU - Zhao, Shijun
AU  - Zhao S
AD  - Department of Diagnostic Radiology, National Cancer Center/National Clinical 
      Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences 
      and Peking Union Medical College, Beijing, China.
FAU - Zhou, Lina
AU  - Zhou L
AD  - Department of Diagnostic Radiology, National Cancer Center/National Clinical 
      Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences 
      and Peking Union Medical College, Beijing, China.
FAU - Huang, Yao
AU  - Huang Y
AD  - Department of Diagnostic Radiology, National Cancer Center/National Clinical 
      Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences 
      and Peking Union Medical College, Beijing, China.
FAU - Wang, Jianwei
AU  - Wang J
AD  - Department of Diagnostic Radiology, National Cancer Center/National Clinical 
      Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences 
      and Peking Union Medical College, Beijing, China.
FAU - Wu, Ning
AU  - Wu N
AD  - PET-CT Center, National Cancer Center/National Clinical Research Center for 
      Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union 
      Medical College, Beijing, China.
AD  - Department of Diagnostic Radiology, National Cancer Center/National Clinical 
      Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences 
      and Peking Union Medical College, Beijing, China.
LA  - eng
PT  - Journal Article
PL  - China
TA  - Ann Transl Med
JT  - Annals of translational medicine
JID - 101617978
PMC - PMC7944332
OTO - NOTNLM
OT  - Computer-assisted image processing
OT  - cancer screening
OT  - computed tomography (CT)
OT  - deep learning
OT  - neural networks (computer)
COIS- Conflicts of Interest: All authors have completed the ICMJE uniform disclosure 
      form (available at http://dx.doi.org/10.21037/atm-20-5060). The authors have no 
      conflicts of interest to declare.
EDAT- 2021/03/13 06:00
MHDA- 2021/03/13 06:01
PMCR- 2021/02/01
CRDT- 2021/03/12 07:20
PHST- 2021/03/12 07:20 [entrez]
PHST- 2021/03/13 06:00 [pubmed]
PHST- 2021/03/13 06:01 [medline]
PHST- 2021/02/01 00:00 [pmc-release]
AID - atm-09-04-291 [pii]
AID - 10.21037/atm-20-5060 [doi]
PST - ppublish
SO  - Ann Transl Med. 2021 Feb;9(4):291. doi: 10.21037/atm-20-5060.