Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage
Objective: Chondrocyte viability (CV) can be measured with the label-free method using second harmonic generation (SHG) and two-photon excitation autofluorescence (TPAF) imaging. To automate the image processing for the label-free CV measurement, we previously demonstrated a two-step deep-learning m...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Osteoarthritis and Cartilage Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665913123000821 |
| _version_ | 1797400867539779584 |
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| author | Hongming Fan Pei Xu Xun Chen Yang Li Zhao Zhang Jennifer Hsu Michael Le Emily Ye Bruce Gao Harry Demos Hai Yao Tong Ye |
| author_facet | Hongming Fan Pei Xu Xun Chen Yang Li Zhao Zhang Jennifer Hsu Michael Le Emily Ye Bruce Gao Harry Demos Hai Yao Tong Ye |
| author_sort | Hongming Fan |
| collection | DOAJ |
| description | Objective: Chondrocyte viability (CV) can be measured with the label-free method using second harmonic generation (SHG) and two-photon excitation autofluorescence (TPAF) imaging. To automate the image processing for the label-free CV measurement, we previously demonstrated a two-step deep-learning method: Step 1 used a U-Net to segment the lacuna area on SHG images; Step 2 used dual CNN networks to count live cells and the total number of cells in extracted cell clusters from TPAF images. This study aims to develop one-step deep learning methods to improve the efficiency of CV measurement. Method: TPAF/SHG images were acquired simultaneously on cartilage samples from rats and pigs using two-photon microscopes and were merged to form RGB color images with red, green, and blue channels assigned to emission bands of oxidized flavoproteins, reduced forms of nicotinamide adenine dinucleotide, and SHG signals, respectively. Based on the Mask R-CNN, we designed a deep learning network and its denoising version using Wiener deconvolution for CV measurement. Results: Using training and test datasets from rat and porcine cartilage, we have demonstrated that Mask R-CNN-based networks can segment and classify individual cells with a single-step processing flow. The absolute error (difference between the measured and the ground-truth CV) of the CV measurement using the Mask R-CNN with or without Wiener deconvolution denoising reaches 0.01 or 0.08, respectively; the error of the previous CV networks is 0.18, significantly larger than that of the Mask R-CNN methods. Conclusions: Mask R-CNN-based deep-learning networks improve efficiency and accuracy of the label-free CV measurement. |
| first_indexed | 2024-03-09T02:01:47Z |
| format | Article |
| id | doaj.art-71a678ad139f49abbb620e2055c2c419 |
| institution | Directory Open Access Journal |
| issn | 2665-9131 |
| language | English |
| last_indexed | 2024-03-09T02:01:47Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Osteoarthritis and Cartilage Open |
| spelling | doaj.art-71a678ad139f49abbb620e2055c2c4192023-12-08T04:46:10ZengElsevierOsteoarthritis and Cartilage Open2665-91312023-12-0154100415Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilageHongming Fan0Pei Xu1Xun Chen2Yang Li3Zhao Zhang4Jennifer Hsu5Michael Le6Emily Ye7Bruce Gao8Harry Demos9Hai Yao10Tong Ye11Department of Bioengineering, Clemson University, SC, USASchool of Computing, Clemson University, SC, USADepartment of Bioengineering, Clemson University, SC, USASchool of Medicine, Yale University, New Haven, CT, USADepartment of Bioengineering, Clemson University, SC, USADepartment of Bioengineering, Clemson University, SC, USA; School of Computing, Clemson University, SC, USADepartment of Bioengineering, Clemson University, SC, USACollege of Medicine, Medical University of South Carolina, Charleston, SC, USADepartment of Bioengineering, Clemson University, SC, USADepartment of Orthopaedics & Physical Medicine, Medical University of South Carolina, Charleston, SC, USADepartment of Bioengineering, Clemson University, SC, USA; Department of Orthopaedics & Physical Medicine, Medical University of South Carolina, Charleston, SC, USA; Department of Oral Health Sciences, Medical University of South Carolina, Charleston, SC, USADepartment of Bioengineering, Clemson University, SC, USA; Department of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, SC, USA; Corresponding author. Department of Bioengineering, Clemson University, Clemson, SC, USA.Objective: Chondrocyte viability (CV) can be measured with the label-free method using second harmonic generation (SHG) and two-photon excitation autofluorescence (TPAF) imaging. To automate the image processing for the label-free CV measurement, we previously demonstrated a two-step deep-learning method: Step 1 used a U-Net to segment the lacuna area on SHG images; Step 2 used dual CNN networks to count live cells and the total number of cells in extracted cell clusters from TPAF images. This study aims to develop one-step deep learning methods to improve the efficiency of CV measurement. Method: TPAF/SHG images were acquired simultaneously on cartilage samples from rats and pigs using two-photon microscopes and were merged to form RGB color images with red, green, and blue channels assigned to emission bands of oxidized flavoproteins, reduced forms of nicotinamide adenine dinucleotide, and SHG signals, respectively. Based on the Mask R-CNN, we designed a deep learning network and its denoising version using Wiener deconvolution for CV measurement. Results: Using training and test datasets from rat and porcine cartilage, we have demonstrated that Mask R-CNN-based networks can segment and classify individual cells with a single-step processing flow. The absolute error (difference between the measured and the ground-truth CV) of the CV measurement using the Mask R-CNN with or without Wiener deconvolution denoising reaches 0.01 or 0.08, respectively; the error of the previous CV networks is 0.18, significantly larger than that of the Mask R-CNN methods. Conclusions: Mask R-CNN-based deep-learning networks improve efficiency and accuracy of the label-free CV measurement.http://www.sciencedirect.com/science/article/pii/S2665913123000821Nonlinear optical microscopyAutofluorescenceSecond harmonic generationDeep learning segmentationChondrocyte viability |
| spellingShingle | Hongming Fan Pei Xu Xun Chen Yang Li Zhao Zhang Jennifer Hsu Michael Le Emily Ye Bruce Gao Harry Demos Hai Yao Tong Ye Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage Osteoarthritis and Cartilage Open Nonlinear optical microscopy Autofluorescence Second harmonic generation Deep learning segmentation Chondrocyte viability |
| title | Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage |
| title_full | Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage |
| title_fullStr | Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage |
| title_full_unstemmed | Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage |
| title_short | Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage |
| title_sort | mask r cnn provides efficient and accurate measurement of chondrocyte viability in the label free assessment of articular cartilage |
| topic | Nonlinear optical microscopy Autofluorescence Second harmonic generation Deep learning segmentation Chondrocyte viability |
| url | http://www.sciencedirect.com/science/article/pii/S2665913123000821 |
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