Publications

2023

1. Automatic Fracture Detection and Characterization in Borehole Images Using Deep Learning-Based Semantic Segmentation

   Andrei Baraian, Vili Kellokumpu, Räty Tomi, and 1 more author

   In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023), 2023

   18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2023 ; Conference date: 19-02-2023 Through 21-02-2023

   Abs

   Fracture analysis represents one of the key investigations that needs to be carried in borehole logs. Identifying fractures, as well as other similar features (like breakouts or foliations) is essential for characterizing the reservoir where the drilling took place. However, identifying and characterizing the fractures from borehole images is a very time and resource consuming task, that require extensive knowledge from geological experts. For this reason, developing semi-automated or automated tools would facilitate and increase the productivity of fracture analysis, since even for one reservoir, experts need to analyze and interpret hundreds of meters of borehole images. This paper presents a deep learning based approach for application of automatic fracture detection and characterization in borehole images, relying on state-of-the-art convolutional neural network for accurate semantic segmentation of fractures. Target images consists of color borehole images, as opposed to acoustic or drill-core images, and uses real world data, both for training the deep learning model and testing the whole system. The system is evaluated by using multiple metrics and the final outputs of the system are the parameters of the sinusoids that define the predicted fractures.

2. Agile and Holistic Medical Software Development: Final report of AHMED project

   Pasi Ahola, Andrei Baraian, Klaus Förger, and 10 more authors

   Feb 2023

   Abs

   AHMED is a co-innovation project (https://www.regops.fi/) with six companies (Atostek, Bittium, Mylab, Solita, Taipuva Consulting and Terveystalo) and two research organisations (University of Helsinki and VTT). This report provides summarized results of the AHMED project focused on research, development and adoption of solutions for efficient production of data-driven applications for healthcare. The project particularly addressed challenges related to the usage of artificial intelligence, machine learning and personal data in regulated medical software. We present experiences and best practices for transition to the new medical device regulation (MDR), AI/ML compliant RegOps models, tools and toolchains for medical software development, healthcare register data usage for innovative applications and traceability approaches for research and AI/ML model development.

2022

1. Computing Particle Size Distribution of Mineral Rocks using Deep Learning-based Instance Segmentation

   Andrei Baraian, Vili Kellokumpu, Janne Paaso, and 2 more authors

   In 2022 10th European Workshop on Visual Information Processing, EUVIP 2022, Oct 2022

2019

1. Improved 3D Perception based on Color Monocular Camera for MAV exploiting Image Semantic Segmentation

   Andrei Baraian, and Sergiu Nedevschi

   In 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP), Oct 2019