Murine femur micro-computed tomography and biomechanical datasets for an ovariectomy-induced osteoporosis model

The development of new effective and safer therapies for osteoporosis, in addition to improved diagnostic and prevention strategies, represents a serious need in the scientific community. Micro-CT image-based analyses in association with biomechanical testing have become pivotal tools in identifying osteoporosis in animal models by assessment of bone microarchitecture and resistance, as well as bone strength. Here, we describe a dataset of micro-CT scans and reconstructions of 15 whole femurs and biomechanical tests on contralateral femurs from C57BL/6JOlaHsd ovariectomized (OVX), resembling human post-menopausal osteoporosis, and sham operated (sham) female mice. Data provided for each mouse include: the acquisition images (.tiff), the reconstructed images (.bmp) and an.xls file containing the maximum attenuations for each reconstructed image. Biomechanical data include an.xls file with the recorded load-displacement, a movie with the filmed test and an.xls file collecting all biomechanical results.This study was funded by Basque Country government under the ELKARTEK program No. kk-2018/00031/BC and No. kk-2019/00093/BC

Attri-VAE: attribute-based interpretable representations of medical images with variational autoencoders

Deep learning (DL) methods where interpretability is intrinsically considered as part of the model are required to better understand the relationship of clinical and imaging-based attributes with DL outcomes, thus facilitating their use in the reasoning behind the medical decisions. Latent space representations built with variational autoencoders (VAE) do not ensure individual control of data attributes. Attribute-based methods enforcing attribute disentanglement have been proposed in the literature for classical computer vision tasks in benchmark data. In this paper, we propose a VAE approach, the Attri-VAE, that includes an attribute regularization term to associate clinical and medical imaging attributes with different regularized dimensions in the generated latent space, enabling a better-disentangled interpretation of the attributes. Furthermore, the generated attention maps explained the attribute encoding in the regularized latent space dimensions. Using the Attri-VAE approach we analyzed healthy and myocardial infarction patients with clinical, cardiac morphology, and radiomics attributes. The proposed model provided an excellent trade-off between reconstruction fidelity, disentanglement, and interpretability, outperforming state-of-the-art VAE approaches according to several quantitative metrics. The resulting latent space allowed the generation of realistic synthetic data in the trajectory between two distinct input samples or along a specific attribute dimension to better interpret changes between different cardiac conditions.This work was partly funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825903 (euCanSHare project)