Abstract
Bone sarcoma treatment employs wide surgical margins to ensure complete tumour excision, sometimes at the expense of patient quality of life. As precision technologies advance, quantitative data to inform the planned accuracy of surgical margins is increasingly important. This study assesses two novel computational methods (M2, M3) to support the co-registration of 2D histologic images within 3D radiological volumes. Replicated evaluation of an established method (M1) is also performed with new data. M2 suits concave bisected sections by assuming a different orientation during tissue bandsaw than M1. M3 handles freeform geometries by using a third measurement to constrain 2D histology images within 3D space without need for geometric bandsaw assumptions. Three computed distances across 38 canine specimen dissections from each method were compared with three corresponding physical measurements collected during laboratory processing. M1 produced a mean error of 0.12 (± 1.7) mm, M2 produced a mean error of 0.06 (± 1.9) mm, while M3 produced a mean error of 0.02 (± 1.5) mm. Mean errors for each method were of similar magnitude to 3D registration error reported in histology co-registration frameworks for other tissues. This study supports a simple low cost opportunity for use of digital histology images to support bone sarcoma radiology interpretation and treatment planning.