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Centro de Inovaç ão em Imagens Médicas, CIIM - PUCPR, Curitiba, Paraná, Brazil, Programa de Pós-Graduaç ão em Informática, PPGIa - PUCPR, Curitiba, Paraná and Brazil

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2 Papers

Bone Segmentation of the Human Body in Computerized Tomographies using Deep Learning

Topics: Machine Learning; Learning with AI Systems; Emerging Technologies in Education for Sustainable Development

Authors: Angelo Antonio Manzatto , Edson Emílio Justino and Edson José Rodrigues Scalabrin

Affiliation: Programa de Pós Graduação em Informática, PPGIa - PUCPR, Curitiba, Paraná, Brazil

Digital Object Identifier: 10.5220/0010891200003182

Proceedings of CSEDU 2022, ISBN: 978-989-758-562-3

Quick Abstract

The segmentation of human body organs in medical imaging is a widely used process to detect and diagnose diseases in medicine and to help students learn human anatomy in education. Despite its significance, segmentation is time consuming and costly because it requires experts in the field, time, and the requisite tools. Following the advances in artificial intelligence, deep learning networks were employed in this study to segment computerized tomography images of the full human body, made available by the Visible Human Project (VHP), which included among 19 classes (18 types of bones and background): cranium, mandible, clavicle, scapula, humerus, radius, ulna, hands, ribs, sternum, vertebrae, sacrum, hips, femur, patella, tibia, fibula, and feet. For the proposed methodology, a VHP male body tomographic base containing 1865 images in addition to the 20 IRCAD tomographic bases containing 2823 samples were used to train deep learning networks of various architectures. Segmentation was tested on the VHP female body base containing 1730 images. Our quantitative evaluation of the results with respect to the overall average Dice coefficient was 0.5673 among the selected network topologies. Subsequent statistical tests demonstrated the superiority of the U-Net network over the other architectures, with an average Dice of 0.6854.

An Ultra-high Definition and Interactive Simulator for Human Dissection in Anatomic Learning

Topics: Gamification for Learning; Virtual and Augmented Learning Environments

Authors: Andrei Rafael Brongel ¹ , William John Pereira Brobouski ¹ , Lucas Murbach Pierin ² , Carlos Gomes ³ , Manoel de Campos Almeida ⁴ and Edson José Rodrigues Justino ⁵

Affiliations: ¹ Programa de Pós-Graduaç ão em Informática, PPGIa - PUCPR, Curitiba, Paraná and Brazil , ² Polytechnic School, Pontifícia Universidade Católica do Paraná - PUCPR, Curitiba, Paraná and Brazil , ³ Medical School, Pontifícia Universidade Católica do Paraná - PUCPR, Curitiba, Paraná and Brazil , ⁴ Centro de Inovaç ão em Imagens Médicas, CIIM - PUCPR, Curitiba, Paraná and Brazil , ⁵ Centro de Inovaç ão em Imagens Médicas, CIIM - PUCPR, Curitiba, Paraná, Brazil, Programa de Pós-Graduaç ão em Informática, PPGIa - PUCPR, Curitiba, Paraná and Brazil

Digital Object Identifier: 10.5220/0007707102840291

Proceedings of CSEDU 2019, ISBN: 978-989-758-367-4

Quick Abstract

This paper presents an ultra-high definition simulator for the teaching of human anatomy in class. The simulator in question consists of hardware and software specially developed for this purpose. The hardware seeks to meet the requirements of real-scale representation of models, visual acuity, color, texture, depth perception e touch-based interactivity. The software, in turn, offers a set of dissecting tools typically used in anatomical studies, in addition to allowing connectivity to educational environments and the Internet. The characteristic that stands out most in this simulator is the fact that it is not an anatomical atlas, but a dissecting table that uses models from real bodies, which differentiates it from most of the simulators and anatomical atlases developed to this end.