Pilvo aortos aneurizmos segmentavimas neuroniniais tinklais kompiuterinės tomografijos nuotraukose

Pilvo aortos aneurizmos diagnostikai ir stebėsenai dažnai naudojami kompiuterinės tomografijos vaizdai. Kontrastinėje tomografijos nuotraukoje kontrastas yra gerai matomas, tačiau automatinis trombo segmentavimas yra daug sudėtingesnė problema dėl aplink trombą esančių panašaus intensyvumo vaizdo taškų, vaizduojančių kitus vidaus organus. Šiame darbe atliekama pilvo aortos kontrasto ir trombo segmentacija naudojant tris neuroninius tinklus: Res-Net-100, DeepLab V3 ir U-Net. Lyginami rezultatai, gauti naudojant skirtingas nuostolių funkcijas, taip pat taikomas programiškai išskaičiuotų trombo žymėjimų paruošimas naudojant morfologines operacijas. Taip pat pritaikomas atsitiktinis neuronų išmetimas ir klasifikavimas naudojant sumažinto lango kompiuterinės tomografijos vaizdus

Pilvo aortos vietos nustatymas krūtinės ląstos tomografinėje nuotraukoje

Kompiuterinė tomografija, kuri naudojama pilvo aortos aneurizmos diagnostikai ir stebėjimui, leidžia vartotojams stebėti aneurizmos būseną paciento kūno skerspjūvio nuotraukų sekoje. Dažnai kompiuterine tomografija grįstiems diagnostikos įrankiams aortos vieta turi būti nurodoma vartotojo. Darbe aprašomas pilvo aortos vidinių taškų identifikavimo būdas be pradinės aortos vietos išankstinio žymėjimo. Darbe sprendžiami uždaviniai: medicinos vaizdų analizės metodų apžvalga bei parinkimas, pirminis vaizdų apdorojimas pašalinant triukšmą bei išskiriant vaizde esančių objektų kontūrus, aortos aptikimo metodo pasiūlymas. Taip pat pasiūlomas aortos trombo aptikimo metodas, kuriam vartotojas turi pateikti sekos vaizdą, kuriame trombas yra matomas, bet jam nereikia nurodyti tikslios ar apytikslės trombo vietos

Data Analysis Methods for Software Systems

Using statistics, econometrics, machine learning, and functional data analysis methods, we evaluate the consequences of the lockdown during the COVID-19 pandemics for wage inequality and unemployment. We deduce that these two indicators mostly reacted to the first lockdown from March till June 2020. Also, analysing wage inequality, we conduct analysis separately for males and females and different age groups.We noticed that young females were affected mostly by the lockdown.Nevertheless, all the groups reacted to the lockdown at some level

The segmentation of the abdominal aortic aneurysm and thrombus in computed tomography images

Computed tomography images are used by the medical experts to diagnose and monitor abdominal aortic aneurysm by analysing slices of computed tomography images. As there is a shortage of automatic software tools for the detection and diagnostics of abdominal aortic aneurysm, the process is often performed manually or semi-automatic, which results in long analysis time and increases the risk of the patient not getting treatment on time. In this work literature analysis is performed, which describes the most frequently used types of medical imaging in the abdominal aortic aneurysm research and the methods of preprocessing and marking of images. The segmentation of abdominal aortic aneurysm is performed by training three neural networks with several loss functions. Various data processing methods are used, including cropping, changing scale and ratio of original image. Dropout is used to increase the accuracy of the segmentations. In addition to this, a method for the segmentation of the abdominal aortic aneurism is suggested, which combines two neural networks that were trained to segment different regions. This way similar segmentation results can be achieved to those results, that were achieved using twice as few patients

The identification of abdominal aorta in chest tomography image

Computer tomography, which is used for diagnostics and monitoring of the abdominal aortic aneurysm, allows the users to observe the abdominal aortic aneurysm in a sequence of cross-sectional images. Often the location of the aorta has to be provided for computer tomography-based applications in diagnostics. The aim of this work is to identify the abdominal aorta without providing its initial location. The objectives include the review of the image analysis methods used for medical images, the preprocessing of images by noise reduction and border extraction, the suggestion of the method for aorta identification as well as the identification of aortic thrombus. In this work the method for aorta identification is created, which does not require any initial information about aorta’s location in an image. The method for the identification of aortic thrombus is also suggested, which requires the initial frame number where thrombus is visible. The methods suggested are first steps to automatic visualisation of the aorta and thrombus, which would be useful in clinical applications. These methods perform well and could be used in further research to extend the model for images after the branching of the aorta