Hyalite Sol-Gel Amoeba: A Physiology-Based Biophysical Model for Segmentation and Biotransformation of Medical Images To 3D Solid-State Characterizing Native Tissue Properties for Patient-Specific and Patient-Appropriate Analysis for Surgical Applications

Introduction: The endeavour to improve medical image segmentation techniques for higher analysis in surgical planning and medical therapeutics is far from becoming a standard of care in clinical practice. Hyalite Sol-Gel Amoeba model based on biophysical sciences apart from performing image segmentation is designed to extract real-world tissue densities for patient-specific and patient-appropriate analysis. Objectives: Amoeba Proteus is a unicellular independent entity, with a nucleus and sol-gel protoplasm enclosed in a membrane. The study presents versatile restructuring anatomy and physiology of the Amoeba Proteus for segmentation of 2D, and 3D medical images based on well-established principles of energy minimization and active contour. It demonstrates how the animalcule glides and advances by throwing pseudopodia driven by phenomenal actin-myosin activity that can segment a region-of-interest, and finally, at the time of apoptosis, its protoplasm and organelles acquire distribution of original image intensities to characterize tissue densities. Methods: This seminal study following a brief review of computer vision science discusses the relationship between optical density and tissue density, and the theory of sol-gel fluid mechanics. The framework of the HSG-Amoeba is described with the segmentation of various skeletal components of the thoracic cage. Results: This being a foundational study to describe the concept of the HSG-Amoeba model it requires the development of a mathematical algorithm to demonstrate its worthiness as a tool for surgical applications. Conclusion: The focus of the study is to present the design and framework of the newly conceived HSG-Amoeba model to segment a medical image and extract tissue densities without altering the original image intensities.