BACKGROUND AND OBJECTIVES: Histological analysis is a gold standard technique for studying impaired skin wound healing. Label-free multiphoton microscopy (MPM) can provide natural image contrast similar to histological sections and quantitative metabolic information using NADH and FAD autofluorescence. However, MPM analysis requires time-intensive manual segmentation of specific wound tissue regions limiting the practicality and usage of the technology for monitoring wounds. The goal of this study was to train a series of convolutional neural networks (CNNs) to segment MPM images of skin wounds to automate image processing and quantification of wound geometry and metabolism. STUDY DESIGN/ MATERIALS AND METHODS: Two CNNs with a 4-layer U-Net architecture were trained to segment unstained skin wound tissue sections and in vivo z-stacks of the wound edge. The wound section CNN used 380 distinct MPM images while the in vivo CNN used 5,848 with both image sets being randomly distributed to training, validation, and test sets following a 70%, 20%, and 10% split. The accuracy of each network was evaluated on the test set of images, and the effectiveness of automated measurement of wound geometry and optical redox ratio were compared with hand traced outputs of six unstained wound sections and 69 wound edge z-stacks from eight mice. RESULTS: The MPM wound section CNN had an overall accuracy of 92.83%. Measurements of epidermal/dermal thickness, wound depth, wound width, and % re-epithelialization were within 10% error when evaluated on six full wound sections from days 3, 5, and 10 post-wounding that were not included in the training set. The in vivo wound z-stack CNN had an overall accuracy of 89.66% and was able to isolate the wound edge epithelium in z-stacks from eight mice across post-wound time points to quantify the optical redox ratio within 5% of what was recorded by manual segmentations. CONCLUSION: The CNNs trained and presented in this study can accurately segment MPM imaged wound sections and in vivo z-stacks to enable automated and rapid calculation of wound geometry and metabolism. Although MPM is a noninvasive imaging modality well suited to imaging living wound tissue, its use has been limited by time-intensive user segmentation. The use of CNNs for automated image segmentation demonstrate that it is possible for MPM to deliver near real-time quantitative readouts of tissue structure and function. Lasers Surg. Med.
BACKGROUND AND OBJECTIVES: Histological analysis is a gold standard technique for studying impaired skin wound healing. Label-free multiphoton microscopy (MPM) can provide natural image contrast similar to histological sections and quantitative metabolic information using NADH and FAD autofluorescence. However, MPM analysis requires time-intensive manual segmentation of specific wound tissue regions limiting the practicality and usage of the technology for monitoring wounds. The goal of this study was to train a series of convolutional neural networks (CNNs) to segment MPM images of skin wounds to automate image processing and quantification of wound geometry and metabolism. STUDY DESIGN/ MATERIALS AND METHODS: Two CNNs with a 4-layer U-Net architecture were trained to segment unstained skin wound tissue sections and in vivo z-stacks of the wound edge. The wound section CNN used 380 distinct MPM images while the in vivo CNN used 5,848 with both image sets being randomly distributed to training, validation, and test sets following a 70%, 20%, and 10% split. The accuracy of each network was evaluated on the test set of images, and the effectiveness of automated measurement of wound geometry and optical redox ratio were compared with hand traced outputs of six unstained wound sections and 69 wound edge z-stacks from eight mice. RESULTS: The MPM wound section CNN had an overall accuracy of 92.83%. Measurements of epidermal/dermal thickness, wound depth, wound width, and % re-epithelialization were within 10% error when evaluated on six full wound sections from days 3, 5, and 10 post-wounding that were not included in the training set. The in vivo wound z-stack CNN had an overall accuracy of 89.66% and was able to isolate the wound edge epithelium in z-stacks from eight mice across post-wound time points to quantify the optical redox ratio within 5% of what was recorded by manual segmentations. CONCLUSION: The CNNs trained and presented in this study can accurately segment MPM imaged wound sections and in vivo z-stacks to enable automated and rapid calculation of wound geometry and metabolism. Although MPM is a noninvasive imaging modality well suited to imaging living wound tissue, its use has been limited by time-intensive user segmentation. The use of CNNs for automated image segmentation demonstrate that it is possible for MPM to deliver near real-time quantitative readouts of tissue structure and function. Lasers Surg. Med.
Authors: Melissa C Skala; Kristin M Riching; Annette Gendron-Fitzpatrick; Jens Eickhoff; Kevin W Eliceiri; John G White; Nirmala Ramanujam Journal: Proc Natl Acad Sci U S A Date: 2007-11-27 Impact factor: 11.205