PMID- 36908135
OWN - NLM
STAT- MEDLINE
DCOM- 20230314
LR  - 20230324
IS  - 1552-4930 (Electronic)
IS  - 1552-4922 (Linking)
VI  - 103
IP  - 3
DP  - 2023 Mar
TI  - Attention Mask R-CNN with edge refinement algorithm for identifying circulating 
      genetically abnormal cells.
PG  - 227-239
LID - 10.1002/cyto.a.24682 [doi]
AB  - Recent studies have suggested that circulating tumor cells with abnormalities in 
      gene copy numbers in mononuclear cell-enriched peripheral blood samples, such as 
      circulating genetically abnormal cells (CACs), can be used as a non-invasive tool 
      to detect patients with benign pulmonary nodules. These cells are identified 
      through fluorescence signals counting by using 4-color fluorescence in situ 
      hybridization (FISH) technology that exhibits high stability, sensitivity, and 
      specificity. When FISH data are analyzed, the overlapping cells and fluorescence 
      noise is a great challenge for identifying of CACs, thereby seriously affecting 
      the efficiency of clinical diagnosis. To address this problem, in this study, we 
      proposed an end-to-end FISH-based method (CACNET) for CAC identification. CACNET 
      achieved nuclear segmentation and counted 4-color staining signals through 
      improved Mask region-based convolutional neural network (R-CNN), followed by cell 
      category (normal cell, deletion cell, gain cell, or CAC) according to 
      pathological criteria. Firstly, the segmentation accuracy of overlapping nuclei 
      was improved by adding an edge constraint head during training. Then, the 
      interference of fluorescence noise was reduced by fusing non-local module to 
      reconstruct the feature extraction network of Mask R-CNN. We trained and tested 
      the proposed model on a dataset comprising 700 frames with 58,083 nuclei. The 
      Accuracy, Sensitivity, and Specificity (overall performance metric for the 
      algorithm) of CAC identification with CACNET were 94.06%, 92.1%, and 99.8%, 
      respectively. Moreover, the developed method exhibited approximately 
      identification speed of approximately 0.22 s per frames. The results showed that 
      the proposed method outperformed the existing CAC identification methods, making 
      it a promising approach for early screening of lung cancer.
CI  - (c) 2022 International Society for Advancement of Cytometry.
FAU - Xu, Xu
AU  - Xu X
AD  - China Academy of Information and Communications Technology, Beijing, China.
FAU - Li, Congsheng
AU  - Li C
AD  - China Academy of Information and Communications Technology, Beijing, China.
FAU - Fan, Xianjun
AU  - Fan X
AD  - Zhuhai Sanmed Biotech Ltd, Zhuhai, Guangdong, China.
FAU - Lan, Xinjie
AU  - Lan X
AD  - Zhuhai Sanmed Biotech Ltd, Zhuhai, Guangdong, China.
FAU - Lu, Xing
AU  - Lu X
AD  - Zhuhai Sanmed Biotech Ltd, Zhuhai, Guangdong, China.
FAU - Ye, Xin
AU  - Ye X
AD  - Zhuhai Sanmed Biotech Ltd, Zhuhai, Guangdong, China.
FAU - Wu, Tongning
AU  - Wu T
AD  - China Academy of Information and Communications Technology, Beijing, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220815
PL  - United States
TA  - Cytometry A
JT  - Cytometry. Part A : the journal of the International Society for Analytical 
      Cytology
JID - 101235694
SB  - IM
MH  - Humans
MH  - In Situ Hybridization, Fluorescence/methods
MH  - *Neural Networks, Computer
MH  - Algorithms
MH  - *Lung Neoplasms/pathology
MH  - Cell Nucleus/pathology
OTO - NOTNLM
OT  - cell identification
OT  - circulating genetically abnormal cells
OT  - convolutional neural network
OT  - non-local module
EDAT- 2023/03/14 06:00
MHDA- 2023/03/15 06:00
CRDT- 2023/03/13 02:13
PHST- 2022/07/04 00:00 [revised]
PHST- 2022/05/09 00:00 [received]
PHST- 2022/07/29 00:00 [accepted]
PHST- 2023/03/13 02:13 [entrez]
PHST- 2023/03/14 06:00 [pubmed]
PHST- 2023/03/15 06:00 [medline]
AID - 10.1002/cyto.a.24682 [doi]
PST - ppublish
SO  - Cytometry A. 2023 Mar;103(3):227-239. doi: 10.1002/cyto.a.24682. Epub 2022 Aug 
      15.