180 citations · 581 across the 31 of their papers we have counts for
33 papers
Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs
Nils Gessert, Julia Krüger, Roland Opfer +5
Multiple sclerosis is an inflammatory autoimmune demyelinating disease that is characterized by lesions in the central nervous system. Typically, magnetic resonance imaging (MRI) i…
4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes
Marcel Bengs, Nils Gessert, Alexander Schlaefer
Tracking and localizing objects is a central problem in computer-assisted surgery. Optical coherence tomography (OCT) can be employed as an optical tracking system, due to its high…
Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification
Marcel Bengs, Nils Gessert, Wiebke Laffers +6
Early detection of cancerous tissue is crucial for long-term patient survival. In the head and neck region, a typical diagnostic procedure is an endoscopic intervention where a med…
Needle tip force estimation by deep learning from raw spectral OCT data
M. Gromniak, N. Gessert, T. Saathoff +1
Purpose. Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside th…
A Deep Learning Approach for Motion Forecasting Using 4D OCT Data
Marcel Bengs, Nils Gessert, Alexander Schlaefer
Forecasting motion of a specific target object is a common problem for surgical interventions, e.g. for localization of a target region, guidance for surgical interventions, or mot…
4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation
Nils Gessert, Marcel Bengs, Julia Krüger +5
Multiple sclerosis lesion activity segmentation is the task of detecting new and enlarging lesions that appeared between a baseline and a follow-up brain MRI scan. While deep learn…
Deep learning with 4D spatio-temporal data representations for OCT-based force estimation
Nils Gessert, Marcel Bengs, Matthias Schlüter +1
Estimating the forces acting between instruments and tissue is a challenging problem for robot-assisted minimally-invasive surgery. Recently, numerous vision-based methods have bee…
4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification
Marcel Bengs, Nils Gessert, Alexander Schlaefer
Autism spectrum disorder (ASD) is associated with behavioral and communication problems. Often, functional magnetic resonance imaging (fMRI) is used to detect and characterize brai…
Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection
Marcel Bengs, Stephan Westermann, Nils Gessert +6
Early detection of head and neck tumors is crucial for patient survival. Often, diagnoses are made based on endoscopic examination of the larynx followed by biopsy and histological…
Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data
Marcel Bengs, Nils Gessert, Matthias Schlüter +1
Purpose. Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. Optical coherence tomography…
Learning Preference-Based Similarities from Face Images using Siamese Multi-Task CNNs
Nils Gessert, Alexander Schlaefer
Online dating has become a common occurrence over the last few decades. A key challenge for online dating platforms is to determine suitable matches for their users. A lot of datin…
Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models
Nils Gessert, Marcel Bengs, Alexander Schlaefer
The initial assessment of skin lesions is typically based on dermoscopic images. As this is a difficult and time-consuming task, machine learning methods using dermoscopic images h…
Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data
Nils Gessert, Maximilian Nielsen, Mohsin Shaikh +2
In this paper, we describe our method for the ISIC 2019 Skin Lesion Classification Challenge. The challenge comes with two tasks. For task 1, skin lesions have to be classified bas…
Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels
Nils Gessert, Martin Gromniak, Marcel Bengs +2
Electroencephalography (EEG) allows for source measurement of electrical brain activity. Particularly for inverse localization, the electrode positions on the scalp need to be know…
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs
Nils Gessert, Alexander Schlaefer
Cardiac left ventricle (LV) quantification provides a tool for diagnosing cardiac diseases. Automatic calculation of all relevant LV indices from cardiac MR images is an intricate…
Spatio-Temporal Deep Learning Models for Tip Force Estimation During Needle Insertion
Nils Gessert, Torben Priegnitz, Thore Saathoff +6
Purpose. Precise placement of needles is a challenge in a number of clinical applications such as brachytherapy or biopsy. Forces acting at the needle cause tissue deformation and…
Deep Transfer Learning Methods for Colon Cancer Classification in Confocal Laser Microscopy Images
Nils Gessert, Marcel Bengs, Lukas Wittig +4
Purpose: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, re…
Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting
Nils Gessert, Thilo Sentker, Frederic Madesta +5
Objective: This work addresses two key problems of skin lesion classification. The first problem is the effective use of high-resolution images with pretrained standard architectur…
Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation
Nils Gessert, Alexander Schlaefer
Typically, deep learning architectures are handcrafted for their respective learning problem. As an alternative, neural architecture search (NAS) has been proposed where the archit…
Towards Automatic Lesion Classification in the Upper Aerodigestive Tract Using OCT and Deep Transfer Learning Methods
Nils Gessert, Matthias Schlüter, Sarah Latus +3
Early detection of cancer is crucial for treatment and overall patient survival. In the upper aerodigestive tract (UADT) the gold standard for identification of malignant tissue is…
Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks
Nils Gessert, Lukas Wittig, Daniel Drömann +3
Histological evaluation of tissue samples is a typical approach to identify colorectal cancer metastases in the peritoneum. For immediate assessment, reliable and real-time in-vivo…
Two-path 3D CNNs for calibration of system parameters for OCT-based motion compensation
Nils Gessert, Martin Gromniak, Matthias Schlüter +1
Automatic motion compensation and adjustment of an intraoperative imaging modality's field of view is a common problem during interventions. Optical coherence tomography (OCT) is a…
Bioresorbable Scaffold Visualization in IVOCT Images Using CNNs and Weakly Supervised Localization
Nils Gessert, Sarah Latus, Youssef S. Abdelwahed +3
Bioresorbable scaffolds have become a popular choice for treatment of coronary heart disease, replacing traditional metal stents. Often, intravascular optical coherence tomography…
Feasibility of a markerless tracking system based on optical coherence tomography
Matthias Schlüter, Christoph Otte, Thore Saathoff +2
Clinical tracking systems are popular but typically require specific tracking markers. During the last years, scanning speed of optical coherence tomography (OCT) has increased to…