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Electrosurgical resection of colorectal polyps - mathematical modelling of processes during medical treatment

Siedlecki Jarosław

Journal of Applied Mathematics and Computational Mechanics

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2023

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Vol. 22, nr 3

43--56

EN

The colonoscopic electrosurgical polypectomy is a very popular surgical procedure in which the colon polyps are removed. In this work, the mathematical description of the electrical and thermal processes proceeding during this procedure has been proposed. The mathematical model contains the specification of the considered domain’s geometry, the system of the partial differential equations that governs heat transfer in the considered particular sub-domains (i.e. polyp, colon and electrode) with the adequate initial-boundary conditions, the system of the differential equations for determination of the electrical potential distribution in the tissue sub-domains, and the definition of the Arrhenius tissue damage integral. Next, the example results of numerical simulations for the proper and incorrect positions of the polyp in the colon are presented. The conclusions are also provided. The proposed research can be helpful for the surgeons to choose the optimal set parameters of the electric current during the endoscopy procedure.

2

Quantitative impact of label noise on the quality of segmentation of brain tumors on MRI scans

Marcinkiewicz Michał, Mrukwa Grzegorz

Annals of Computer Science and Information Systems

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2019

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Vol. 18

61--65

EN

Over the last few years, deep learning has proven to be a great solution to many problems, such as image or text classification. Recently, deep learning-based solutions have outperformed humans on selected benchmark datasets, yielding a promising future for scientific and real-world applications. Training of deep learning models requires vast amounts of high quality data to achieve such supreme performance. In real-world scenarios, obtaining a large, coherent, and properly labeled dataset is a challenging task. This is especially true in medical applications, where high-quality data and annotations are scarce and the number of expert annotators is limited. In this paper, we investigate the impact of corrupted ground-truth masks on the performance of a neural network for a brain tumor segmentation task. Our findings suggest that a) the performance degrades about 8% less than it could be expected from simulations, b) a neural network learns the simulated biases of annotators, c) biases can be partially mitigated by using an inversely-biased dice loss function.

3

Numerical simulation of the freezing process in biological tissue with tumor

Dziewoński M., Paruch M.

Zeszyty Naukowe Katedry Mechaniki Stosowanej / Politechnika Śląska

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2006

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z. 26

87-92

EN

In the paper the numerical simulation of freezing process of tissue with tumor is presented. In particular the action of external cylindrical cryoprobe is analyzed. On the stage of numerical simulations the finite element method (FEM) is applied. The computations have been done using the MSC MARC/MENTAT code supplemented by additional procedures taking into account the cyclic boundary conditions and temperature-dependent thermophysical parameters of the tissue and tumor [2].