The effect of diabetes and tissue depth on adipose chamber size and plantar soft tissue features.

BACKGROUND

Plantar ulceration is a serious complication of diabetes. However, the mechanism of injury initiating ulceration remains unclear. The unique structure of the plantar soft tissue includes superficial and deep layers of adipocytes contained in septal chambers, however, the size of these chambers has not been quantified in diabetic or non-diabetic tissue. Computer-aided methods can be leveraged to guide microstructural measurements and differences with disease status.

METHODS

Adipose chambers in whole slide images of diabetic and non-diabetic plantar soft tissue were segmented with a pre-trained U-Net and area, perimeter, and minimum and maximum diameter of adipose chambers were measured. Whole slide images were classified as diabetic or non-diabetic using the Axial-DeepLab network, and the attention layer was overlaid on the input image for interpretation.

RESULTS

Non-diabetic deep chambers were 90 %, 41 %, 34 %, and 39 % larger in area (26,954 ± 2428 µm2 vs 14,157 ± 1153 µm2), maximum (277 ± 13 µm vs 197 ± 8 µm) and minimum (140 ± 6 µm vs 104 ± 4 µm) diameter, and perimeter (405 ± 19 µm vs 291 ± 12 µm), respectively, than the superficial (p < 0.001). However, there was no significant difference in these parameters in diabetic specimens (area 18,695 ± 2576 µm2 vs 16627 ± 130 µm2, maximum diameter 221 ± 16 µm vs 210 ± 14 µm, minimum diameter 121 ± 8 µm vs 114 ± 7 µm, perimeter 341 ± 24 µm vs 320 ± 21 µm). Between diabetic and non-diabetic chambers, only the maximum diameter of the deep chambers differed (221 ± 16 µm vs 277 ± 13 µm). The attention network achieved 82 % accuracy on validation, but the attention resolution was too coarse to identify meaningful additional measurements.

CONCLUSIONS

Adipose chamber size differences may provide a basis for plantar soft tissue mechanical changes with diabetes. Attention networks are promising tools for classification, but additional care is required when designing networks for identifying novel features.

DATA AVAILABILITY: All images, analysis code, data, and/or other resources required to replicate this work are available from the corresponding author upon reasonable request.