40 citations · 68 across the 9 of their papers we have counts for
9 papers
End-To-End Prediction of Knee Osteoarthritis Progression With Multi-Modal Transformers
Egor Panfilov, Simo Saarakkala, Miika T. Nieminen +1
Knee Osteoarthritis (KOA) is a highly prevalent chronic musculoskeletal condition with no currently available treatment. The manifestation of KOA is heterogeneous and prediction of…
Unsupervised denoising for sparse multi-spectral computed tomography
Satu I. Inkinen, Mikael A. K. Brix, Miika T. Nieminen +2
Multi-energy computed tomography (CT) with photon counting detectors (PCDs) enables spectral imaging as PCDs can assign the incoming photons to specific energy channels. However, P…
Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning
Egor Panfilov, Simo Saarakkala, Miika T. Nieminen +1
Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials. Prior art focused on…
Machine Learning Based Texture Analysis of Patella from X-Rays for Detecting Patellofemoral Osteoarthritis
Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala
Objective is to assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. We used lateral view knee…
Automated Detection of Patellofemoral Osteoarthritis from Knee Lateral View Radiographs Using Deep Learning: Data from the Multicenter Osteoarthritis Study (MOST)
Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala
Objective: To assess the ability of imaging-based deep learning to predict radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs. Design: Knee latera…
A Lightweight CNN and Joint Shape-Joint Space (JS2) Descriptor for Radiological Osteoarthritis Detection
Neslihan Bayramoglu, Miika T. Nieminen, Simo Saarakkala
Knee osteoarthritis (OA) is very common progressive and degenerative musculoskeletal disease worldwide creates a heavy burden on patients with reduced quality of life and also on s…
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis
Neslihan Bayramoglu, Aleksei Tiulpin, Jukka Hirvasniemi +2
The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the abil…
Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation
Egor Panfilov, Aleksei Tiulpin, Stefan Klein +2
Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA) research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data is…
An automatic regularization method: An application for 3D X-ray micro-CT reconstruction using sparse data
Zenith Purisha, Sakari S. Karhula, Juuso Ketola +5
X-ray tomography is a reliable tool for determining the inner structure of 3D object with penetrating X-rays. However, traditional reconstruction methods such as FDK require dense…