831 citations · 861 across the 9 of their papers we have counts for
11 papers
Stochastic Siamese MAE Pretraining for Longitudinal Medical Images
Taha Emre, Arunava Chakravarty, Thomas Pinetz +9
Temporally aware image representations are crucial for capturing disease progression in 3D volumes of longitudinal medical datasets. However, recent state-of-the-art self-supervise…
Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation
José Morano, Guilherme Aresta, Dmitrii Lachinov +3
Deep learning has become a valuable tool for the automation of certain medical image segmentation tasks, significantly relieving the workload of medical specialists. Some of these…
Learning Spatio-Temporal Model of Disease Progression with NeuralODEs from Longitudinal Volumetric Data
Dmitrii Lachinov, Arunava Chakravarty, Christoph Grechenig +2
Robust forecasting of the future anatomical changes inflicted by an ongoing disease is an extremely challenging task that is out of grasp even for experienced healthcare profession…
Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification
Botond Fazekas, Dmitrii Lachinov, Guilherme Aresta +3
Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the lea…
SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors
Botond Fazekas, Guilherme Aresta, Dmitrii Lachinov +4
Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina. Achieving automated, anatomically coherent retinal layer segme…
Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT
Dmitrii Lachinov, Philipp Seeboeck, Julia Mai +2
In medical imaging, there are clinically relevant segmentation tasks where the output mask is a projection to a subset of input image dimensions. In this work, we propose a novel c…
Cephalometric Landmark Regression with Convolutional Neural Networks on 3D Computed Tomography Data
Dmitry Lachinov, Alexandra Getmanskaya, Vadim Turlapov
In this paper, we address the problem of automatic three-dimensional cephalometric analysis. Cephalometric analysis performed on lateral radiographs doesn't fully exploit the struc…
Knowledge Distillation for Brain Tumor Segmentation
Dmitrii Lachinov, Elena Shipunova, Vadim Turlapov
The segmentation of brain tumors in multimodal MRIs is one of the most challenging tasks in medical image analysis. The recent state of the art algorithms solving this task is base…
CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation
A. Emre Kavur, N. Sinem Gezer, Mustafa Barış +24
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have intr…
The Coherent Point Drift for Clustered Point Sets
Dmitry Lachinov, Vadim Turlapov
The problem of non-rigid point set registration is a key problem for many computer vision tasks. In many cases the nature of the data or capabilities of the point detection algorit…
Glioma Segmentation with Cascaded Unet
Dmitry Lachinov, Evgeny Vasiliev, Vadim Turlapov
MRI analysis takes central position in brain tumor diagnosis and treatment, thus it's precise evaluation is crucially important. However, it's 3D nature imposes several challenges,…