26 citations · 30 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…