Machine learning approaches for cancer bone segmentation from micro computed tomography images

Many types of cancers such as multiple myeloma cause bone destruction, resulting in pain and fractures in patients and increased fatality. To quantify the degree of bone disease caused by cancer and analyse treatment response for bone repairing, accurate knowledge of the volumetry of all lesions is needed. To this end, this study proposes to apply two main approaches to the segmentation of bone lesions in cancer-induced bone disease from Micro Computed Tomography (μCT) images - structured forest-based edge detection approach and deep learning approach. A fast edge detection approach with structured forest, an extension of [1], is applied to identify the volumetry of all lesions in mice tibia, where the obtained results are evaluated against the manually labelled data, demonstrating the efficiency of the compared approaches. The Gaussian processes (Convnet GP) approach has achieved the best performance among the compared approaches, with 99.6% intersection of union and 99.7% precision. Our results demonstrate that the developed approach provides a reasonable delineation of the samples, showing the great potential towards fully automatic bone tumour segmentation.