User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy

Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to provide satisfactory result, and post-processing corrections are often needed. Semi-automatic segmentation methods are designed to overcome these problems by combining physicians’ expertise and computers’ potential. This study evaluates two semi-automatic segmentation methods with different types of user interactions, named the “strokes” and the “contour”, to provide insights into the role and impact of human-computer interaction. Two physicians participated in the experiment. In total, 42 case studies were carried out on five different types of organs at risk. For each case study, both the human-computer interaction process and quality of the segmentation results were measured subjectively and objectively. Furthermore, different measures of the process and the results were correlated. A total of 36 quantifiable and ten non-quantifiable correlations were identified for each type of interaction. Among those pairs of measures, 20 of the contour method and 22 of the strokes method were strongly or moderately correlated, either directly or inversely. Based on those correlated measures, it is concluded that: (1) in the design of semi-automatic segmentation methods, user interactions need to be less cognitively challenging; (2) based on the observed workflows and preferences of physicians, there is a need for flexibility in the interface design; (3) the correlated measures provide insights that can be used in improving user interaction design

Detecção automática de pontos cefalométricos em imagens faciais: uma abordagem aplicada na estimação de idade e sexo a partir da norma frontal

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2019.Métodos forenses para estimação das informações de indivíduos são constantemente utilizados por peritos em cenários reais. O processo forense de estimação de idade e sexo necessita de um profissional capacitado e abordagens que normalmente exigem a presença física do indivíduo para a execução dos procedimentos periciais. A pornografia infantil é uma atividade ilícita que conta com a facilidade da internet para o compartilhamento de imagens e vídeos tornando fácil a disseminação deste tipo de conteúdo e dificultando a identificação e perícia do material. Pesquisas sobre técnicas de reconhecimento de padrões e aprendizado de máquina em visão computacional permitem o desenvolvimento de metodologias baseadas em fotoantropometria para a identificação de informações antropométricas apenas utilizando imagens faciais digitais. Com o avanço da tecnologia e com o aumento do volume de dados, os profissionais necessitam de alternativas para processar as informações antropométricas de indivíduos apenas analisando arquivos digitais. Este trabalho tem como objetivo desenvolvimento de metodologias de identificação automática de pontos cefalométricos em imagens faciais, gerar medidas fotoantropométricas e propor uma solução computacional para auxiliar os profissionais forenses para a estimação da idade e sexo em uma base de dados com 105 mil de imagens faciais. O trabalho proposto para identificação de pontos cefalométricos obteve precisão similar com as marcações realizadas por especialistas com resultado de erro médio da distância (em pixels) normalizada de 0:014 contra 0:009 de dispersão média dos testes entre especialistas. Os resultados obtidos demonstram significância no processo de estimação de dados antropométricos, utilizando imagens faciais com redes neurais convolucionais e medidas fotoantropométricas faciais. A proposta desenvolvida obteve resultado de 99; 2% de acerto para estimação de sexo. Para estimação de maior/menor de 18 anos o resultado F1 score foi de 0; 926 enquanto para maior/menor de 14 anos foi de 0; 957. Por último, estimação de idade, a proposta obteve um resultado MAE de 1; 42 utilizando uma amostra de indivíduos com idades entre 2 a 22 anos.Forensic methods of estimating information from individuals are constantly used by experts in real scenarios. The forensic process of age and sex estimation requires an expert and approaches that normally need the physical presence of the individual for the execution of the expert procedures. Child pornography is an illicit activity that relies on the ease of the internet to acess and disseminate this type of content, making it difficult to identify and exploit the material. Research on pattern recognition techniques and machine learning in computer vision allow the development of methodologies based on photo-anthropometry for identification of anthropometric information using facial images. The advancement of technology and the increasing process of the data volume, the experts need alternatives to be inferring the age and of individuals by analyzing digital files only. This work has the goal to develop methodologies for automatic identification of cephalometric points in facial images, generate photo-anthropometric measurements and propose a computational solution to assist forensic professionals to estimate age and sex in a database with thousands of images. The proposed work to identify cephalometric landmarks obtained similar accuracy with the manual points made by experts with result of normalized average error (in pixels) of 0:014 versus 0:009 of average dispersion by experts. The results present significance in the estimation process of anthropometric data using facial images with convolutional neural networks and facial photo-anthropometric measurements. The developed proposal obtained 99:2% positive results for sex estimation. For the estimation of over 18 years old the result of F1 was 0:926 while for over 14 years old it was 0:957. Finally, the age estimate, the proposal obtained a MAE result of 1:42 using a sample of individuals over ages from 2 to 22 years old

Automatic Craniofacial Structure Detection on Cephalometric Images

Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Cephalometric analysis is divided in two categories, manual and automatic approaches. The manual approach is limited in accuracy and repeatability due to differences in inter- and intra-personal marking. In this paper, we have attempted to develop and test a novel method for automatic localization of craniofacial structures based on the detected edges in the region of interest. Before edge detection of the particular region, the region was filtered by adaptive non local filter for noise removal by keeping the edge information undisturbed. According to the gray-scale feature at the different regions of the cephalograms, modified Canny edge detection algorithm for obtaining tissue contour was proposed. With the application of morphological opening and edge linking approaches, an improved bidirectional contour tracing methodology was proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method