Segmentation of Patient Images in the Neonatal Intensive Care Unit

Dossso, Yasmina Souley; Bekele, Amente; Nizami, Shermeen; Aubertin, Cheryl; Greenwood, Kim; Harrold, JoAnn; Green, James R.

doi:10.1109/lsc.2018.8572169

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…Recent improvements in digital video processing have allowed increasing automation, but most of the abovementioned studies need to be manually initialized in order to select the considered region (whole baby or limbs). Promising results were recently obtained with CNN but the method was only applied within a controlled environment setup (Dossso et al 2018). Furthermore, most of the proposed methods extract global motion information (i.e.…”

Section: Discussionmentioning

confidence: 99%

Video and audio processing in paediatrics: a review

Cabon¹,

Porée²,

Simon³

et al. 2019

Physiol. Meas.

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Objective: Video and sound acquisition and processing technologies have seen great improvements in recent decades, with many applications in the biomedical area. The aim of this paper is to review the overall state of the art of advances within these topics in paediatrics and to evaluate their potential application for monitoring in the neonatal intensive care unit (NICU). Approach: For this purpose, more than 150 papers dealing with video and audio processing were reviewed. For both topics, clinical applications are described according to the considered cohorts—full-term newborns, infants and toddlers or preterm newborns. Then, processing methods are presented, in terms of data acquisition, feature extraction and characterization. Main results: The paper first focuses on the exploitation of video recordings; these began to be automatically processed in the 2000s and we show that they have mainly been used to characterize infant motion. Other applications, including respiration and heart rate estimation and facial analysis, are also presented. Audio processing is then reviewed, with a focus on the analysis of crying. The first studies in this field focused on induced-pain cries and the newest ones deal with spontaneous cries; the analyses are mainly based on frequency features. Then, some papers dealing with non-cry signals are also discussed. Significance: Finally, we show that even if recent improvements in digital video and signal processing allow for increased automation of processing, the context of the NICU makes a fully automated analysis of long recordings problematic. A few proposals for overcoming some of the limitations are given.

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Section: Discussionmentioning

confidence: 99%

Video and audio processing in paediatrics: a review

Cabon¹,

Porée²,

Simon³

et al. 2019

Physiol. Meas.

View full text Add to dashboard Cite

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“…Yasmina et al (2018) proposed a model for segmenting images of videos of patients in the NICU. The proposed method uses convolution neural network and obtained the test accuracy of about 93% (16). S.M.…”

Section: Review Of Related Workmentioning

confidence: 99%

Implementation of an Intelligent Neonatal Monitoring System Using Raspberry Pi

M¹,

Rajagopal²,

M³

et al. 2022

ECS Trans.

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An incubator is a biomedical apparatus used to keep neonates if they are ill and in need of further investigations. This is also useful for observation and care of newborn babies who were premature or of low birth weight. There will be loss of lives of neonates if the incubator is not appropriately monitored and this may even lead to accidents. In view of these, it has been proposed to implement an Intelligent Monitoring System for Neonatal Intensive Care Unit (NICU). For monitoring the premature infants continuously it requires sensors. It has been learnt that the temperature, breathing rate, body movements, loudness, or cry are some of the features to be monitored in incubator. Hence, in this paper, a wireless communication and body area network based system that constantly examine the vital physiological parameters of the baby kept in the NICU has been discussed. The analog signals from the sensors are processed using a Raspberry Pi. The system has been developed with Raspberry Pi B+ module as the controller. Sensors are used to monitor the physiological parameters and Pi camera is used to detect the baby movements. Global System for Mobile Communications (GSM) module is incorporated in the proposed system to transmit the parameters via a short message service (SMS). Hence, this system offers a suitable method for parents and nurses to monitor premature infants.

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“…A research work proposed an edgebased deep learning model through IoT for healthcare systems used the cloud to the edge computing model and try to used CNN for classifications [40]. In a study [41], image segmentation technique for Neonatal ICU is described. They used a transfer learning approach to use a trained CNN to process video overhead RGB-D camera.…”

Section: Review Of Some Recent State-of-the-artsmentioning

confidence: 99%

Insights of Artificial Intelligence to Stop Spread of COVID-19

Sufian

Jat

Banerjee

2020

Studies in Big Data

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, a pandemic that has pushed down human civilization in a severe threat. As viruses of COVID-19 like diseases are transferable from human to human, so it becomes very challenging to stop spreading these pandemics. These challenges are not only limited to the treatment of infected patients but also maintaining systematic social distancing to stop spreading the disease. However, maintaining social distancing is not entirely possible everywhere, like in hospitals, emergency sectors, etc. Some critical issues such as: carefully handling the Intensive Care Unite (ICU), patient care, hygienic practice, and systematic social distancing have become very necessary to slow down the spread of the new virus as appropriate vaccines or drugs are not yet available. In this time of crisis, Artificial Intelligence (AI) could assists in many more ways in addition to assisting diagnosis, drug or vaccine discovery. Therefore, this AI, especially algorithms of machine learning, deep learning, and computer vision along with edge computing and IoT technologies could be smart solutions for such challenges. This chapter brings such solutions through some insights of AI to assist to stop these COVID-19 like pandemics.

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Segmentation of Patient Images in the Neonatal Intensive Care Unit

Cited by 10 publications

References 13 publications

Video and audio processing in paediatrics: a review

Video and audio processing in paediatrics: a review

Implementation of an Intelligent Neonatal Monitoring System Using Raspberry Pi

Insights of Artificial Intelligence to Stop Spread of COVID-19

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