Boundary Attention Mapping (BAM): Fine-grained saliency maps for
  segmentation of Burn Injuries
Mahla Abdolahnejad Justin Lee Hannah Chan Alex Morzycki Olivier Ethier Anthea Mo Peter X. Liu Joshua N. Wong Colin Hong Rakesh Joshi
Burn injuries can result from mechanisms such as thermal, chemical, and
electrical insults. A prompt and accurate assessment of burns is essential for
deciding definitive clinical treatments. Currently, the primary approach for
burn assessments, via visual and tactile observations, is approximately 60%-80%
accurate. The gold standard is biopsy and a close second would be non-invasive
methods like Laser Doppler Imaging (LDI) assessments, which have up to 97%
accuracy in predicting burn severity and the required healing time. In this
paper, we introduce a machine learning pipeline for assessing burn severities
and segmenting the regions of skin that are affected by burn. Segmenting 2D
colour images of burns allows for the injured versus non-injured skin to be
delineated, clearly marking the extent and boundaries of the localized
burn/region-of-interest, even during remote monitoring of a burn patient. We
trained a convolutional neural network (CNN) to classify four severities of
burns. We built a saliency mapping method, Boundary Attention Mapping (BAM),
that utilises this trained CNN for the purpose of accurately localizing and
segmenting the burn regions from skin burn images. We demonstrated the
effectiveness of our proposed pipeline through extensive experiments and
evaluations using two datasets; 1) A larger skin burn image dataset consisting
of 1684 skin burn images of four burn severities, 2) An LDI dataset that
consists of a total of 184 skin burn images with their associated LDI scans.
The CNN trained using the first dataset achieved an average F1-Score of 78% and
micro/macro- average ROC of 85% in classifying the four burn severities.
Moreover, a comparison between the BAM results and LDI results for measuring
injury boundary showed that the segmentations generated by our method achieved
91.60% accuracy, 78.17% sensitivity, and 93.37% specificity.