Algorithms for wound segmentation
Evaluation of the UNet convolutional neural network, specifically trained for wound segmentation and formulated in:
U-Net: Convolutional Networks for Biomedical Image Segmentation. O. Ronneberger, P. Fisher and T. Brox, in Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015.
Evaluation of the Double-UNet convolutional neural network, specifically trained for wound segmentation and formulated in:
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation. D. Jha, M. Riegler, D. Johansen, P. Halvorsen and H. Johansen, in the IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020.
Evaluation of the GSC convolutional neural network, specifically trained for wound segmentation and formulated in:
Promising crack segmentation method based on gated skip connection. M. Jabreel and M. Abdel-Nasser, in Electronic Letters, Volume 56, Pages 493-495, 2020.
Algorithms for staples segmentation
Evaluation of the UNet convolutional neural network, specifically trained for staples segmentation and formulated in:
U-Net: Convolutional Networks for Biomedical Image Segmentation. O. Ronneberger, P. Fisher and T. Brox, in Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015.
Evaluation of the Double-UNet convolutional neural network, specifically trained for staples segmentation and formulated in:
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation. D. Jha, M. Riegler, D. Johansen, P. Halvorsen and H. Johansen, in the IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020.
Evaluation of the GSC convolutional neural network, specifically trained for staples segmentation and formulated in:
Promising crack segmentation method based on gated skip connection. M. Jabreel and M. Abdel-Nasser, in Electronic Letters, Volume 56, Pages 493-495, 2020.