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

Automatic detection, segmentation and classification of abdominal aortic aneurysm using deep learning

This study is focused on developing an automated algorithm for the detection and segmentation of Abdominal Aortic Aneurysm (AAA) region in CT Angiography images. The outcome of this research will offer great assistance for radiologists to detect the AAA region and estimate its volume in CT datasets efficiently. In addition, suitable treatment strategies can also be suggested based on the classification of the AAA severity and measurement of the aorta diameter. This research takes the initiative by exploring and applying deep learning architecture in the study of AAA detection and segmentation, which has never been done by other researchers before in AAA problems. The findings from this study will also act as a reference for other similar future works. Deep Belief Network (DBN) is applied for the purpose of AAA detection and severity classification in this study. Optimum parameters for training the DBN are determined for the training data from the selected dataset. AAA region can be successfully segmented from the CT images and the result is comparable to the existing method with advantage over the existing method that the proposed method is fully automatic and added with auto detection and classification features. The limitation of the trained DBN in AAA detection accuracy can be improved by incorporating and adjusting the detection probability threshold and shape constraints. In future, the DBN can be further enhanced by adding and training it with more data which covers a wider variety of features, as well as extending its capability to perform detailed segmentation on AAA region

Konferencijos „Lietuvos magistrantų informatikos ir IT tyrimai“ darbai

The conference "Lithuanian MSc Research in Informatics and ICT" is a venue to present research of Lithuanian MSc theses in informatics and ICT. The aim of the event is to raise skills of MSc and other students, familiarize themselves with the research of other students, encourage their interest in scientific activities. Students from Kaunas University of Technology and Vilnius University will give their presentations at the conference