A method for automated masking and plantar pressure analysis using segmented computed tomography scans.

BACKGROUND: Plantar pressure, a common gait and foot biomechanics measurement, is typically analyzed using proprietary commercial software packages. Regional plantar pressure analysis is often reported in terms of underlying bony geometry, and recent advances in image processing and accessibility have made computed tomography, radiographs, magnetic resonance imaging, or other imaging methods more popular for incorporating bone analyses in biomechanics. RESEARCH QUESTION: Can a computed tomography-based regional mask provide comparable regional analysis to commercial plantar pressure software and can the increased flexibility of an in-house method obtain additional insight from common measurements? METHODS: A plantar pressure analysis method was developed based on bony geometry from computed tomography scans to calculate peak pressure, pressure time integral incorporating sub-peak values, force time integral, pressure gradient, and pressure gradient angle. Static and dynamic plantar pressure were acquired for 4 subjects (male, 65 ± 2.4 years). Plantar pressure variables were calculated using commercial and computed tomography-based systems. RESULTS: Dynamic peak pressure, pressure time integral, and force-time integral computed using the bone-based software was 5 % (9kPa), 7 % (0.3kPa-s) and 13 % (0.3 N-s) different than the commercial software on average. Region masks of the metatarsals and toes differed between commercial and computed tomography-based software due to subject-specific bone geometry and toe shape. Pressure time integral values incorporating sub-peak pressure were higher and demonstrated higher relative hindfoot values compared to those without. Removing step-on frames to static pressure analysis decreased forefoot pressures. Regional maps of peak pressure and maximum pressure gradient demonstrate different peak locations. SIGNIFICANCE: Computed tomography-based regional masks are comparable to commercial masks. Inclusion of static step-on frames and sub-peak pressures may change regional plantar pressure patterns. Differences in location of maximum pressure gradient and peak pressure may be useful for assessing subject specific injury risk.

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.