AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers

To date, endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. Prolonged Fluoroscopic exposure is harmful for the patient and the clinician, and may lead to severe post-operative sequlae such as the development of cancer. Meanwhile, the use of interventional Ultrasound has gained popularity, due to its well-known benefits of small spatial footprint, fast data acquisition, and higher tissue contrast images. However, ultrasound images are hard to interpret, and it is difficult to localise vessels, catheters, and guidewires within them. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences. The network architecture was inspired by the Attention in Attention mechanism, temporal tracking networks, and introduced a novel 3D segmentation head that performs 3D deconvolution across time. In order to facilitate training of such deep learning networks, we introduce a new data synthesis pipeline that used physics-based catheter insertion simulations, along with a convolutional ray-casting ultrasound simulator to produce synthetic ultrasound images of endovascular interventions. The proposed method is validated on a hold-out validation dataset, thus demonstrated robustness to ultrasound noise and a wide range of scanning angles. It was also tested on data collected from silicon-based aorta phantoms, thus demonstrated its potential for translation from sim-to-real. This work represents a significant step towards safer and more efficient endovascular surgery using interventional ultrasound.Comment: This work has been submitted to the IEEE for possible publicatio

INFLUENCE OF XYLENE ON THE TEXTURAL FEATURES OF THE HYBRID GEL MATERIALS

Contains special characters which cannot be displayed

Synthesis and characterization of sol-gel mesoporous organosilicas functionalized with amine groups

In this work we report on the synthesis of porous amine functionalized organosilica materials prepared by co-condensation of tetraethyl orthosilicate (TEOS) and bis-[3-(trimethoxyosilyl)propyl] amine (BTPA). The gels were prepared through a one-step sol-gel process catalyzed by the - NH- groups of BTPA. A block copolymer poly(ethylene glycol)-block-poly(propylene glycol)-block-poly(ethylene glycol) (P123) was employed as a porogen using the surfactant template method. The resultant materials have been characterized by nitrogen gas sorption, powder X-ray diffraction, FT-IR, SEM, Si-29 MAS NMR, C-13 CP MAS NMR and TG/DTA analysis. In order to examine the potential of these materials as adsorbents for heavy metals, Hg(II) adsorption experiments were also performed. The hybrids showed mesoporous disordered structure as evidenced by N2 adsorption-desorption isotherms and XRD patterns. Adsorption of Hg(II) ions from aqueous solutions showed high capacities suggesting that these materials could be used as adsorbents for Hg(II) ions in acid solutions. (C) 2013 Elsevier B.V. All rights reserved

Abstracts Of The Proceedings And The Posters From The Third Scientific Session Of The Medical College Of Varna

October 2-3, 201