COMPUTATIONAL MODELING OF CELLULAR EFFECTS POST-IRRADIATION WITH LOW- AND HIGH-LET PARTICLES AND DIFFERENT ABSORBED DOSES

The use of computational methods to improve the understanding of biological responses to various types of radiation is an approach where multiple parameters can be modelled and a variety of data is generated. This study compares cellular effects modelled for low absorbed doses against high absorbed doses. The authors hypothesized that low and high absorbed doses would contribute to cell killing via different mechanisms, potentially impacting on targeted tumour radiotherapy outcomes. Cellular kinetics following irradiation with selective low- and high-linear energy transfer (LET) particles were investigated using the Virtual Cell (VC) radiobiology algorithm. Two different cell types were assessed using the VC radiobiology algorithm: human fibroblasts and human crypt cells. The results showed that at lower doses (0.01 to 0.2 Gy), all radiation sources used were equally able to induce cell death (p\u3e0.05, ANOVA). On the other hand, at higher doses (1.0 to 8.0 Gy), the radiation response was LET and dose dependent (p\u3c0.05, ANOVA). The data obtained suggests that the computational methods used might provide some insight into the cellular effects following irradiation. The results also suggest that it may be necessary to re-evaluate cellular radiation-induced effects, particularly at low doses that could affect therapeutic effectiveness

Image segmentation for human motion analysis: methods and applications

Human motion analysis is closely connected with the development of computational techniques capable of automatically identify objects represented in image sequences, track and analyse its movement. Feature extraction is generally the first step in the study of human motion in image sequences which is strictly related to human motion modelling [1]. Next step is feature correspondence, where the problem of matching features between two consecutives image frames is addressed. Finally high level processing can be used in several applications of Computer Vision like, for instance, in the recognition of human movements, activities or poses. This work will focus in the study of image segmentation methods and applications for human motion analysis. Image segmentation methods related to human motion need to deal with several challenges such as: dynamic backgrounds, for instance when the camera is in motion; lighting conditions that can change along the image sequences; occlusion problems, when the subject does not remain inside the workspace; or image sequences with more than one subject in the workspace at the same time. It is not easy to develop methods which can deal with all thes

Methods to automatically build Point Distribution Models for objects like hand palms and faces represented in images

In this work we developed methods to automatically extract significant points of objects like hand palms and faces represented in images that can be used to build Point Distribution Models automatically. These models are further used to segment the modelled objects in new images, through the use of Active Shape Models or Active Appearance Models. These models showed to be efficient in the segmentation of objects, but had as drawback the fact that the labelling of the landmark points was usually manually made and consequently time consuming. Thus, in this paper we describe some methods capable to extract significant points of objects like hand palms and compare the segmentation results in new images

Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review

Radiation oncology for prostate cancer is important as it can decrease the morbidity and mortality associated with this disease. Planning for this modality of treatment is both fundamental, time-consuming and prone to human-errors, leading to potentially avoidable delays in start of treatment. A fundamental step in radiotherapy planning is contouring of radiation targets, where medical specialists contouring, i.e., segment, the boundaries of the structures to be irradiated. Automating this step can potentially lead to faster treatment planning without a decrease in quality, while increasing time available to physicians and also more consistent treatment results. This can be framed as an image segmentation task, which has been studied for many decades in the fields of Computer Vision and Machine Learning. With the advent of Deep Learning, there have been many proposals for different network architectures achieving high performance levels. In this review, we searched the literature for those methods and describe them briefly, grouping those based on Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). This is a booming field, evidenced by the date of the publications found. However, most publications use data from a very limited number of patients, which presents an obstacle to deep learning models training. Although the performance of the models has achieved very satisfactory results, there is still room for improvement, and there is arguably a long way before these models can be used safely and effectively in clinical practice. (c) 2020, Springer Science+Business Media, LLC, part of Springer Nature

Automated algorithm for carotid lumen segmentation and 3D reconstruction in B-mode images

The B-mode image system is one of the most popular systems used in the medical area; however it imposes several difficulties in the image segmentation process due to low contrast and noise. Although these difficulties, this image mode is often used in the study and diagnosis of the carotid artery diseases.In this paper, it is described the a novel automated algorithm for carotid lumen segmentation and 3-D reconstruction in B- mode images

Detecção de Faces em Imagens baseada na Identificação da Pele e dos Olhos

Uma das principais áreas de desenvolvimento do domínio da Visão Computacional é a da detecção de faces em imagens. Das várias metodologias existentes nesta área, apresentam-se duas neste artigo: uma baseada na detecção de zonas de pele e uma segunda baseada num modelo protótipo deformável para detecção dos olhos e extracção das suas características. A detecção de zonas de pele permite segmentar imagens de faces considerando regiões nas quais os valores dos seus pixéis, em termos de probabilidade, se assemelham aos exibidos pela pele. No entanto, como esta metodologia não produz informação suficiente para concluir um processo de detecção de faces em imagens com elevada confiança, usa-se em complemento um modelo protótipo deformável para os olhos, que adequadamente posicionado próximo de pequenas regiões detectadas no interior dos segmentos previamente identificados como associados a pele, permite detectar a presença dos olhos e consequentemente validar ou não a existência de uma face. Este artigo apresenta a descrição das referidas metodologias e de alguns resultados experimentais obtidos a partir de implementações desenvolvidas em Matlab