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A fast graph-based algorithm for automated segmentation of subcutaneous and visceral adipose tissue in 3D abdominal computed tomography images

Kucybala Iwona, Tabor Zbislaw, Ciuk Szymon, Chrzan Robert, Urbanik Andrzej, Wojciechowski Wadim

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 2

729--739

EN

The aim of the study was to create an accurate method of automated subcutaneous (SAT) and visceral (VAT) adipose tissue detection basing on three-dimensional (3D) computed tomography (CT) scans. One hundred and forty abdominal CT examinations were analysed. An algorithm for automated detection of SAT and VAT consisted of following steps: thresholding of an analysed image, detection of a patient's body region, separation of SAT and VAT. The algorithm was sequentially applied to each 2D axial slice of a 3D examination. To assess the accuracy of the proposed method, automated and manual segmentations (performed by two readers) of SAT and VAT were compared using Dice similarity coefficient (DSC) and average Hausdorff distance (AHD). Mean DSC was equal to 99.6% ± 0.4% for SAT and 99.6% ± 0.5% for VAT, which was equal to DSC obtained for comparison between both readers. In 90% of cases DSC was equal or above 99.0% and the minimal DSC was 97.6%. AHD equalled to 0.04 ± 0.06 for SAT and 0.13 ± 0.23 for VAT (automated vs. manual segmentations), while AHD for comparison of two manual segmentations was 0.03 ± 0.07 for SAT and 0.09 ± 0.20 for VAT. The processing time for a single slice was 0.16 s for an automated segmentation and 510 min for a manual segmen- tation. The processing time of an entire 3D stack (around 40 2D slices) was on average 6.5 s. Our algorithm for the automated detection of SAT and VAT on 3D CT scans has the same accuracy as manual segmentation and performs equally well for both adipose tissue compartments.

2

Performance of an automated process model discovery – the logistics process of a manufacturing company

Halaška Michal, Šperka Roman

Engineering Management in Production and Services

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2019

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Vol. 11, no. 2

106--118

EN

The simulation and modelling paradigms have significantly shifted in recent years under the influence of the Industry 4.0 concept. There is a requirement for a much higher level of detail and a lower level of abstraction within the simulation of a modelled system that continuously develops. Consequently, higher demands are placed on the construction of automated process models. Such a possibility is provided by automated process discovery techniques. Thus, the paper aims to investigate the performance of automated process discovery techniques within the controlled environment. The presented paper aims to benchmark the automated discovery techniques regarding realistic simulation models within the controlled environment and, more specifically, the logistics process of a manufacturing company. The study is based on a hybrid simulation of logistics in a manufacturing company that implemented the AnyLogic framework. The hybrid simulation is modelled using the BPMN notation using BIMP, the business process modelling software, to acquire data in the form of event logs. Next, five chosen automated process discovery techniques are applied to the event logs, and the results are evaluated. Based on the evaluation of benchmark results received using the chosen discovery algorithms, it is evident that the discovery algorithms have a better overall performance using more extensive event logs both in terms of fitness and precision. Nevertheless, the discovery techniques perform better in the case of smaller data sets, with less complex process models. Typically, automated discovery techniques have to address scalability issues due to the high amount of data present in the logs. However, as demonstrated, the process discovery techniques can also encounter issues of opposite nature. While discovery techniques typically have to address scalability issues due to large datasets, in the case of companies with long delivery cycles, long processing times and parallel production, which is common for the industrial sector, they have to address issues with incompleteness and lack of information in datasets. The management of business companies is becoming essential for companies to stay competitive through efficiency. The issues encountered within the simulation model will be amplified through both vertical and horizontal integration of the supply chain within the Industry 4.0. The impact of vertical integration in the BPMN model and the chosen case identifier is demonstrated. Without the assumption of smart manufacturing, it would be impossible to use a single case identifier throughout the entire simulation. The entire process would have to be divided into several subprocesses.

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Automated detection of uterine contractions in tocography signals – Comparison of algorithms

Horoba K., Wrobel J., Jezewski J., Kupka T., Roj D., Jezewski M.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

610--618

EN

Monitoring of uterine contractile activity enables to control the progress of labor. Automated detection of contractions is an integral part of the signal analysis implemented in computer- aided fetal surveillance system. Comparison of four algorithms for automated detection of uterine contractions in the signal of uterine mechanical activity is presented. Three algorithms are based generally on analysis of the frequency distribution of signal values. The fourth method relies on analyzing the rate of changes of the uterine activity signal. The reference data in form of beginning and end of contraction episodes were provided by human experts. Obtained results show that all algorithms were capable to detect above 91% reference contractions, and less than 7% of recognized patterns were false. Two algorithms can be distinguished as providing a higher performance expressed by the sensitivity of 95% and the positive predictive value of 97%. Such results could be obtained by optimization of contraction validation criteria.

4

Efficiency of automated detection of uterine contraction using tocography

Horoba K., Wrobel J., Jezewski J., Kupka T., Czabanski R., Roj D., Jezewski M.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

207--214

EN

Monitoring of uterine contractile activity enables to control the progress of labour. Automated detection of contractions is to be an integral part of the signal analysis implemented in computer aided fetal surveillance system. Evaluation of efficiency of three algorithms for automated detection of uterine contractions in the signal of uterine mechanical activity is presented. These algorithms are based generally on analysis of the frequency distribution of signal values. The reference data in form of beginning and end of contraction episodes were obtained from human expert. Obtained results showed high efficiency of the algorithms tested where the best one ensured the sensitivity and positive predictive value equal to 92.2 and 97.2, respectively.

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Rozpoznawanie rodzaju ruchu na podstawie odczytów sensorów urządzeń mobilnych

Smołka J., Skublewska-Paszkowska M.

Logistyka

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2014

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nr 6

9713--9721

PL

W wielu sytuacjach istotne jest rejestrowanie tras pokonywanych przez pojazdy należące do firm. Narzędziem, które może posłużyć do realizacji tego zadania są zdobywające dużą popularność urządzenia mobilne – w szczególności smartfony. Istotnym zagadnieniem związanym z tym problemem jest automatyczne wykrywanie jazdy samochodem oraz odróżnienie tego ruchu od innych typowych rodzajów ruchu użytkownika urządzenia mobilnego. W artykule omówiono wyniki wstępnych pomiarów przyspieszenia oraz prędkości. Uwzględniono jazdę samochodem po różnych rodzajach nawierzchni, chód oraz bieg. Dla każdego z nich obliczano wskaźniki takie jak średnia, wariancja czy wartości ekstremalne. W pracy zaproponowano serię trzech kryteriów: prędkość, zakres wartości przyspieszenia oraz wariancję przyspieszenia wzdłuż osi Z, które pozwoliły na zidentyfikowanie rodzaju ruchu na podstawie wartości uzyskanych przez przyjęte wskaźniki. Dodatkowo przedstawiono aplikację mobilną stworzoną przez autorów w celu przeprowadzenia pomiarów, a także scharakteryzowano sposób prowadzenia obliczeń i pomiarów.

EN

For many businesses it is important to track the movement of their motor vehicles. Smartphones and other mobile devices, which are gaining huge popularity in recent years, can be used for this purpose. An important issue connected with this use of mobile devices is being able to automatically detect vehicle movement. It is also important to be able to distinguish vehicle movement apart from a mobile device user’s other typical movements, such as walking and running. This paper presents preliminary results of acceleration and speed acquisition for different movement types. Driving (on smooth and uneven surfaces), walking and running are taken into account. For each movement type, different measures are computed including: average, variance and extreme values. A set of three criteria was proposed: Z axis acceleration range, its variation and device’s speed. The criteria allow for identifying movement type. Additionally ,a mobile application created for the purpose of this paper and the method of registering and transforming data are described.

6

System automatycznego wykrywania i lokalizacji raka stercza na obrazach perfuzyjnej tomografii komputerowej

Śmietański J., Tadeusiewicz R.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

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2010

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T. 14, z. 1

133-143

PL

Obecnie stosowane procedury diagnostyczne w kierunku wykrycia bądź wykluczenia raka prostaty u mężczyzn są niewystarczające i często bywają zawodne. Nadzieję na zwiększenie skuteczności diagnozy w szczególnie trudnych przypadkach daje technika perfuzyjnej tomografii komputerowej. Metoda ta, będąca wciąż w fazie rozwoju, pozwala na pomiar parametrów przepływu krwi przez badaną tkankę, co uwidaczniane jest na barwnych dwuwymiarowych obrazach, tzw. "mapach parametrycznych". W pracy przedstawiono metodologię i algorytmy umożliwiające automatyzację interpretacji takich właśnie obrazów prostaty. Automatyzacja ta może nie tylko skrócić czas i zmniejszyć koszty diagnozy, ale przede wszystkim ułatwia podjęcie obiektywnej decyzji, niezależnej od subiektywnych ocen zależnych od doświadczenia czy indywidualnych właściwości wzroku diagnosty. Zaproponowana procedura została przetestowana na licznej grupie obrazów pochodzących od rzeczywistych pacjentów, a otrzymane rezultaty wskazują na możliwość stworzenia kompleksowego systemu pozwalającego na zwiększenie skuteczności i pewności stawianej diagnozy.

EN

Detection and localization of the prostate cancer is difficult problem in general case. For this purpose the new method of medical imaging named perfusion computed tomography (p-CT) can be used. Nevertheless images registered by means of p-CT technology are difficult for interpretation, especially when interpretation must be earned by computer instead of experienced professional radiologist. In paper new algorithms for p-CT images automatic interpretation are presented and discussed. Using proposed algorithms both detection and localization of the prostate cancer can be performed. After general description of proposed methods illustrative case study is presented. For proper solution of the problem under consideration the original method for region of interest (ROI) localization is proposed. Such method named "life belt method" can be assessed as simple and effective and therefore it can be recommended for analysis of perfusion computed tomography prostate cancer images.