SpheroidJ: An Open-Source Set of Tools for Spheroid Segmentation.

BACKGROUND AND OBJECTIVES: Spheroids are the most widely used 3D models for studying the effects of different micro-environmental characteristics on tumour behaviour, and for testing different preclinical and clinical treatments. In order to speed up the study of spheroids, imaging methods that automatically segment and measure spheroids are instrumental; and, several approaches for automatic segmentation of spheroid images exist in the literature. However, those methods fail to generalise to a diversity of experimental conditions. The aim of this work is the development of a set of tools for spheroid segmentation that works in a diversity of settings. METHODS: In this work, we have tackled the spheroid segmentation task by first developing a generic segmentation algorithm that can be easily adapted to different scenarios. This generic algorithm has been employed to reduce the burden of annotating a dataset of images that, in turn, has been employed to train several deep learning architectures for semantic segmentation. Both our generic algorithm and the constructed deep learning models have been tested with several datasets of spheroid images where the spheroids were grown under several experimental conditions, and the images acquired using different equipment. RESULTS: The developed generic algorithm can be particularised to different scenarios; however, those particular algorithms fail to generalise to different conditions. By contrast, the best deep learning model, constructed using the HRNet-Seg architecture, generalises properly to a diversity of scenarios. In order to facilitate the dissemination and use of our algorithms and models, we present SpheroidJ, a set of open-source tools for spheroid segmentation. CONCLUSIONS: In this work, we have developed an algorithm and trained several models for spheroid segmentation that can be employed with images acquired under different conditions. Thanks to this work, the analysis of spheroids acquired under different conditions will be more reliable and comparable; and, the developed tools will help to advance our understanding of tumour behaviour.

Proximal femoral growth plate mechanical behavior: Comparison between different developmental stages.

In long bones the growth plate is a cartilaginous structure located between the epiphysis and the diaphysis. This structure regulates longitudinal growth and helps determine the structure of mature bone through the process of endochondral ossification. During human growth the femur's proximal growth plate experiences changes in its morphology that may be related to its mechanical environment. Thus, in order to test this hypothesis from a computational perspective, a finite element analysis on a proximal femur was performed on which we modeled different physeal geometries corresponding to the shapes acquired for this structure in a child between the ages of five to eleven. Results show augmented Von Mises stress values with increasing irregularities in physeal geometry, whereas displacement decreased with increased irregularities in the growth plate's morphology. Such observations suggest that growth plate's shape changes follows a possible mechanical adaptation on imposed loads to sustain a person's increasing body mass during growth.