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3D Printable Vascular Networks Generated by Accelerated Constrained Constructive Optimization for Tissue Engineering.
One of the greatest challenges in fabricating artificial tissues and organs is the incorporation of vascular networks to support the biological requirements of the embedded cells, encouraging tissue formation and maturation. With the advent of 3D printing technology, significant progress has been made with respect to generating vascularized artificial tissues. Current algorithms to generate arterial/venous trees are computationally expensive and offer limited freedom to optimize the resulting structures. Furthermore, there is no method for algorithmic generation of vascular networks that can recapitulate the complexity of the native vasculature for more than two trees, and export directly to a 3D printing format. Here, we report such a method, using an accelerated constructive constrained optimization approach, by decomposing the process into construction, optimization, and collision resolution stages. The new approach reduces computation time to minutes at problem sizes where previous implementations have reported days. With the optimality criterion of maximizing the volume of useful tissue which could be grown around such a network, an approach of alternating stages of construction and batch optimization of all node positions is introduced and shown to yield consistently more optimal networks. The approach does not place a limit on the number of interpenetrating networks that can be constructed in a given space; indeed we demonstrate a biomimetic, liver-like tissue model. Methods to account for the limitations of 3D printing are provided, notably the minimum feature size and infill at sharp angles, through padding and angle reduction, respectively.EPSRC Doctoral Training Partner-ship Award (EP/N509620/1)
EPSRC (EP/R511675/1 & EP/N509620/1)
Isaac Newton Trust
Rosetrees Trust (M787).
Cambridge Trust
CONACyT (Mexico)
EPSRC Cambridge & Cranfield Doctoral Training Centre in Ultra Precision (EP/K503241/1
Komputerowa analiza obrazów z endoskopu bezprzewodowego dla diagnostyki medycznej.
Jednym z badań medycznych stosowanych w diagnostyce chorób przewodu
pokarmowego jest bezprzewodowa endoskopia kapsułkowa. Wynikiem badania
jest film, którego interpretacja przeprowadzana przez lekarza wymaga dużego
skupienia uwagi, jest długotrwała i męcząca. Wyniki interpretacji nie są powtarzalne
– zależą od wiedzy i doświadczenia konkretnego lekarza.
Przedmiotem niniejszej monografii są opracowane przez autora metody
numeryczne, których celem jest analiza obrazów cyfrowych z endoskopu bezprzewodowego zwiększające powtarzalność, wiarygodność oraz obiektywizm
diagnozy medycznej.Wireless capsule endoscopy is one of the medical tests used in diagnosis of
gastrointestinal disorders. A result is a video of internal lumen of gastrointestinal
tract which interpretation carried out by an expert gastroenterologist requires a lot
of attention and is time consuming. The final diagnosis is rarely reproducible – it
depends on the knowledge and experience of the diagnostic experience of the
expert. The subject of this monograph is presentation and validation of novel
algorithms for wireless endoscope video analysis whose purpose is to improve the
reproducibility, reliability and objectivity of medical diagnosis