13 citations · 15 across the 6 of their papers we have counts for
9 papers
Predictive Multi-Landmark OCT Tracking for Increased Motion Robustness
Konrad Reuter, Suresh Guttikonda, Chaitali Uday Karekar +2
Optical coherence tomography is a promising modality for markerless motion tracking due to its high spatial resolution and inherent depth perception. However, existing OCT-based tr…
Tracking Any Point Methods for Markerless 3D Tissue Tracking in Endoscopic Stereo Images
Konrad Reuter, Suresh Guttikonda, Sarah Latus +4
Minimally invasive surgery presents challenges such as dynamic tissue motion and a limited field of view. Accurate tissue tracking has the potential to support surgical guidance, i…
Self-supervised learning for classifying paranasal anomalies in the maxillary sinus
Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker +9
Purpose: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised…
Tissue Classification During Needle Insertion Using Self-Supervised Contrastive Learning and Optical Coherence Tomography
Debayan Bhattacharya, Sarah Latus, Finn Behrendt +4
Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Th…
Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus
Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker +8
Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult…
Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus
Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker +8
Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techn…
Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus
Debayan Bhattacharya, Benjamin Tobias Becker, Finn Behrendt +10
Using deep learning techniques, anomalies in the paranasal sinus system can be detected automatically in MRI images and can be further analyzed and classified based on their volume…
Self-Supervised U-Net for Segmenting Flat and Sessile Polyps
Debayan Bhattacharya, Christian Betz, Dennis Eggert +1
Colorectal Cancer(CRC) poses a great risk to public health. It is the third most common cause of cancer in the US. Development of colorectal polyps is one of the earliest signs of…
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…