Interactive Tele-Radiological Segmentation Systems for Treatment and Diagnosis

Zimeras, Stelios; Gortzis, Lefteris G.

doi:10.1155/2012/713739

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…The real benefit of online learning is to reach wider target participants from different geographical, time, social and cultural background [10]. Online learning is also useful for participants in maintaining quality of life, increase independence, enrich medical knowledge, and improve health conditions [11].…”

Section: Benefit Of Online Learningmentioning

confidence: 99%

Assessment of Online Learning Application for Health Education

Muljo¹,

Perbangsa²,

Pardamean³

2019

Int. J. Onl. Eng.

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<p>Online learning has become a model, learning strategies, and the preferred channel in education around the world because it is not restricted by time and place. The development of online learning in supporting the success of health education programs for early detection of cervical cancer is the right solution to improve health workers knowledge. To measure user acceptance of the online learning applications, the study used Technology Acceptance Model (TAM) approach. Data collected from questionnaires then analyzed by calculating the frequency (proportion) of the total value, the interpretation of a score by looking for index% and interval. The final result of the calculation shows that all respondents agreed and strongly agreed with all four factors measured. The conclusions are the users believe that the use of learning online applications can reduce the task effort, improve job performance, give positive idea to use the technology, and consciously and expressed desire to run the online learning process in the future.<strong></strong></p>

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Section: Benefit Of Online Learningmentioning

confidence: 99%

Assessment of Online Learning Application for Health Education

Muljo¹,

Perbangsa²,

Pardamean³

2019

Int. J. Onl. Eng.

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“…Sectional Medical Images (SMIs), including CT and MRI, are the main objects of medical image diagnosis at present [ 1 , 2 ]. They can be used to obtain any viewing angle via multiplanar reconstruction (MPR) and maximum/minimum/average intensity projection (MIP/MinIP/AVG or MIPs for short) [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Internet Sharing Scheme for Sectional Medical Images according to Existing Hospital Network Facilities and Basic Information Security Rules

Qiao

et al. 2020

Journal of Healthcare Engineering

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Background. With the outbreak of COVID-19, large-scale telemedicine applications can play an important role in the epidemic areas or less developed areas. However, the transmission of hundreds of megabytes of Sectional Medical Images (SMIs) from hospital’s Intranet to the Internet has the problems of efficiency, cost, and security. This article proposes a novel lightweight sharing scheme for permitting Internet users to quickly and safely access the SMIs from a hospital using an Internet computer anywhere but without relying on a virtual private network or another complex deployment. Methods. A four-level endpoint network penetration scheme based on the existing hospital network facilities and information security rules was proposed to realize the secure and lightweight sharing of SMIs over the Internet. A “Master-Slave” interaction to the interactive characteristics of multiplanar reconstruction and maximum/minimum/average intensity projection was designed to enhance the user experience. Finally, a prototype system was established. Results. When accessing SMIs with a data size ranging from 251.6 to 307.04 MB with 200 kBps client bandwidth (extreme test), the network response time to each interactive request remained at approximately 1 s, the original SMIs were kept in the hospital, and the deployment did not require a complex process; the imaging quality and interactive experience were recognized by radiologists. Conclusions. This solution could serve Internet medicine at a low cost and may promote the diversified development of mobile medical technology. Under the current COVID-19 epidemic situation, we expect that it could play a low-cost and high-efficiency role in remote emergency support.

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“…Especially for the radiotherapy planning, the object is to focus on the radiation as closely as possible to the target volume, while avoiding side effects in healthy tissues. 3D visualization of target volume, organs at risk and simulated radiation dose allows an interactive optimization of treatment planning [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Brain segmentation tools under uncertain conditions for radiotherapy treatment planning

Zimeras¹

2019

Biomed Res Clin Prac

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Objective: Radiotherapy Treatment Planning (RTP) is a procedure to plan the irradiation treatment which is usually simulated on a conventional simulator before applying it on the patient. The main goal is to deliver the adequate irradiation dose to a tumour without causing severe damage to surrounding normal and health tissues. Material and methods:The major weaknesses of current RTP systems come from their rendering methods since most of them use surface rendering rather than volume rendering. All target objects and other critical organs are required to be modelled with interactive contouring slice by slice. The sizes of the segmentation objects are not accurate and some of small but critical organs sometimes maybe neglected.Results: Image segmentation is currently used into several medical imaging applications that involve diagnosis or treatment. Segmentation of volumes is an essential tool for the radiation therapy treatment of the cancer. One of the key organs that must be protected during the irradiation treatment is the brain. Nowadays, high resolution computed tomography (CT) data are required to perform accurate 3D treatment planning, and therefore there is the demand for quick but at the same time accurate segmentation tools. Inappropriate contours results have been performed for the cases where uncertain conditions are appeared (like position of the body in the table and metal material of the bed). Conclusions:In this work we presented an algorithm that can be used for the accurate semi-automatic segmentation of the brain in three dimensions (3D) from CT images. Our method, which is currently in clinical use, is basically composed from an edge detection algorithm and statistical extreme values techniques (outliers).

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Interactive Tele-Radiological Segmentation Systems for Treatment and Diagnosis

Cited by 5 publications

References 25 publications

Assessment of Online Learning Application for Health Education

Assessment of Online Learning Application for Health Education

A Lightweight Internet Sharing Scheme for Sectional Medical Images according to Existing Hospital Network Facilities and Basic Information Security Rules

Brain segmentation tools under uncertain conditions for radiotherapy treatment planning

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