Dr. Alfredo Illanes

@article{S_hn_2026,

title = {Ex Vivo Differentiation of Cartilage Degeneration Severity and Subchondral Bone Using Vibroacoustic Signals from Instrument–Tissue Interaction},

author = {T Sühn and M Costa and N Esmaeili and M Spiller and A Boese and J Bertrand and M Friebe and A Illanes and CH Lohmann},

url = {http://dx.doi.org/10.3390/s26154874

https://pubmed.ncbi.nlm.nih.gov/42590649/, PubMed},

doi = {10.3390/s26154874},

year  = {2026},

date = {2026-08-01},

urldate = {2026-08-01},

journal = {Sensors},

volume = {26},

number = {15},

pages = {4874},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{diagnostics16121780,

title = {Feasibility of Vibroacoustic Sensing for Detection of Peritoneal Entry During Laparoscopic Access: A Pilot Study in a Human Body Donor},

author = {M Spiller and R Urrutia and N Esmaeili and A Boese and T Neumuth and A Illanes and S Turial},

url = {https://www.mdpi.com/2075-4418/16/12/1780

https://pubmed.ncbi.nlm.nih.gov/42351438/, PubMed},

doi = {10.3390/diagnostics16121780},

year  = {2026},

date = {2026-06-09},

urldate = {2026-06-09},

journal = {Diagnostics},

volume = {16},

number = {12},

pages = {1780},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {Background/Objectives: Establishing laparoscopic access remains a critical and complication-prone step in minimally invasive surgery. Previous work has shown that proximal vibroacoustic sensing can identify peritoneal puncture events in porcine cadavers. The present pilot study evaluated whether these findings translate to human anatomy under controlled, ex vivo conditions. Methods: A vibroacoustic sensing prototype was proximally attached to a standard Veress needle during 14 insertions into a fresh human body donor (within 48 h post-mortem). An endoscope was introduced laterally to provide visual ground truth of peritoneal entry. Vibroacoustic signals were recorded at the proximal end of the instrument. Time–frequency analyses, transient excitation detection, and statistical comparisons were performed to assess whether (1) peritoneal puncture can be identified in the vibroacoustic signal, (2) signal phases and dynamics correspond to those previously observed in porcine cadavers, and (3) peritoneal punctures can be statistically differentiated from non-peritoneal events. Results: All 14 peritoneal punctures were identifiable in the vibroacoustic signal under the experimental conditions. Characteristic signal phases previously described in porcine tissue, including transient excitation associated with cavity entry, were consistently reproduced with comparable temporal and spectral profiles. Statistical analyses demonstrated group-level differences between peritoneal and non-peritoneal events, and the peritoneal puncture was the highest-energy event of its insertion in 13 of 14 cases (92.9%). Conclusions: Under the controlled ex vivo conditions of this single-donor pilot study, vibroacoustic sensing was feasible for identifying peritoneal puncture in human tissue and reproduced signal dynamics observed in porcine models. To our knowledge, this is the first demonstration of the proximal vibroacoustic sensing concept on a human body donor and the first cross-species replication of the previously reported puncture phase structure, establishing an important translational stepping stone between animal cadaver studies and in vivo investigations. The study demonstrates feasibility rather than clinical reliability: the single-donor design and the retrospective annotation framework limit generalizability. Prospective validation in living patients, across multiple subjects and operators, is required before clinical deployment.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{urrutia2025,

title = {Needle Puncture Detection Using Vibroacoustic Sensing in Layered Phantoms},

author = {R Urrutia and F Ayman and A Boese and C Hansen and A Illanes},

doi = {10.1515/cdbme-2025-0329},

year  = {2025},

date = {2025-12-01},

urldate = {2025-12-01},

journal = {Current Directions in Biomedical Engineering},

volume = {11},

issue = {2},

pages = {112-115},

abstract = {This study evaluates the use of vibroacoustic (VA)sensing to detect material transitions during needle insertions into layered synthetic phantoms. Experiments were conducted using foams with different pore densities and configurations including internal cavities. Time-domain and wavelet analyses showed that denser foams produced more frequent vibrational events,  while  less  dense  foams  resulted  in  sparser  signals. In setups with elastic membranes and cavities, differences in time–frequency patterns were observed: high-frequency activity appeared after membrane rupture but was absent in cavity insertions. These results support the use of VA sensing to identify mechanical changes during minimally invasive procedures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Pickert_2025,

title = {Perpendicular Vascular Changes in NBI-CE of Laryngeal Lesions: Diagnostic Accuracy, Reproducibility, and Common Pitfalls},

author = {P Pickert and A Giers and A Lux and VA Papaioannou and N Esmaeili and J Hagenah and A Illanes and A Boese and C Arens and N Davaris},

url = {http://dx.doi.org/10.3390/diagnostics15233051

https://pubmed.ncbi.nlm.nih.gov/41374432/, PubMed},

doi = {10.3390/diagnostics15233051},

year  = {2025},

date = {2025-11-01},

urldate = {2025-11-01},

journal = {Diagnostics},

volume = {15},

number = {23},

pages = {3051},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{urrutia_exploring_2025,

title = {Exploring Deep Clustering Methods in Vibro-Acoustic Sensing for Enhancing Biological Tissue Characterization},

author = {R Urrutia and D Espejo and M Guerra and K Vio and T Sühn and N Esmaeili and A Boese and P Fuentealba and A Illanes and C Hansen and V Poblete},

url = {https://ieeexplore.ieee.org/document/10981752/},

doi = {10.1109/ACCESS.2025.3566280},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {IEEE Access},

volume = {13},

pages = {80395–80406},

abstract = {Nonlinear dimensionality reduction techniques, often referred to as manifold learning, are increasingly valuable for data visualization and unsupervised clustering. In the context of surgery and medicine, these methods facilitate the analysis of complex datasets, enabling pattern recognition in surgical data. This study explores the characterization of six tissue types through manifold learning and unsupervised clustering, utilizing vibro-acoustic (VA) signals collected from manual palpation experiments. A wireless sensor mounted at the tip of a surgical instrument was used to acquire 1,680 VA signals, which were processed using Fourier transform and cepstral analysis for feature extraction. We assessed the performance of two dimensionality reduction techniques: uniform manifold approximation and projection (UMAP) and variational autoencoder (VAE). Results indicate that cepstral features combined with UMAP yield superior clustering performance compared to VAE, achieving higher classification accuracy ( 92 % vs. 87 % ) and better-defined clusters with greater compactness. The observed differences in performance are linked to the intrinsic properties of the tissues, particularly surface characteristics such as friction and moisture, which affect signal consistency. Additionally, we compared our approach with previous works, including a study utilizing the same dataset, where our methodology demonstrated improved accuracy. Future research will focus on refining the VAE model, increasing the diversity of tissue samples, and validating the proposed approach in real surgical settings to enhance its applicability in minimally invasive surgery.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Spiller_2024,

title = {Enhancing Veress Needle Entry with Proximal Vibroacoustic Sensing for Automatic Identification of Peritoneum Puncture},

author = {M Spiller and N Esmaeili and T Sühn and A Boese and S Turial and AA Gumbs and R Croner and M Friebe and A Illanes},

url = {http://dx.doi.org/10.3390/diagnostics14151698

https://pubmed.ncbi.nlm.nih.gov/39125574/, PubMed},

doi = {10.3390/diagnostics14151698},

year  = {2024},

date = {2024-08-01},

urldate = {2024-08-01},

journal = {Diagnostics},

volume = {14},

number = {15},

pages = {1698},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{urrutia2024variational,

title = {Variational Autoencoder feature clustering for tissue classification in robotic palpation},

author = {R Urrutia and D Espejo and T Sühn and M Guerra and P Fuentealba and V Poblete and A Boese and A Illanes},

doi = {10.1515/cdbme-2024-0123},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Current Directions in Biomedical Engineering},

volume = {10},

issue = {1},

pages = {89},

abstract = {Minimally Invasive Robotic Surgery (MIRS) has emerged as a transformative approach in surgical practice, offering reduced patient trauma and enhanced precision. However, challenges persist, including the loss of tactile feedback for surgeons. This study explores the application of machine learning algorithms, specifically variational autoencoders, in vibro-acoustic (VA) signal analysis to address this issue. Our comparative analysis evaluates the potential of supervised learning in surgical data analysis, contributing to advancements in surgical technology. Despite achieving an accuracy of 81%, our results indicate opportunities for further refinement, considering the superior accuracies reported in previous studies. This research underscores the importance of innovative approaches in medical data analysis for optimizing patient care in minimally invasive surgery.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Esmaeili_2023,

title = {Contact Endoscopy – Narrow Band Imaging (CE-NBI) data set for laryngeal lesion assessment},

author = {N Esmaeili and N Davaris and A Boese and A Illanes and N Navab and M Friebe and C Arens},

url = {http://dx.doi.org/10.1038/s41597-023-02629-7

https://pubmed.ncbi.nlm.nih.gov/37865668/, PubMed},

doi = {10.1038/s41597-023-02629-7},

year  = {2023},

date = {2023-10-01},

urldate = {2023-10-01},

journal = {Scientific Data},

volume = {10},

number = {1},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{S_hn_2023,

title = {Vibro-acoustic sensing of tissue-instrument-interactions allows a differentiation of biological tissue in computerised palpation},

author = {T Sühn and N Esmaeili and M Spiller and M Costa and A Boese and J Bertrand and A Pandey and C Lohmann and M Friebe and A Illanes},

url = {http://dx.doi.org/10.1016/j.compbiomed.2023.107272

https://pubmed.ncbi.nlm.nih.gov/37515873/, PubMed},

doi = {10.1016/j.compbiomed.2023.107272},

year  = {2023},

date = {2023-09-01},

urldate = {2023-09-01},

journal = {Computers in Biology and Medicine},

volume = {164},

pages = {107272},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{urrutia_clustering_2023,

title = {Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions},

author = {R Urrutia and D Espejo and N Evens and M Guerra and T Sühn and A Boese and C Hansen and P Fuentealba and A Illanes and V Poblete},

url = {https://www.mdpi.com/1424-8220/23/23/9297

https://pubmed.ncbi.nlm.nih.gov/38067671/, PubMed},

doi = {10.3390/s23239297},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Sensors},

volume = {23},

number = {23},

pages = {9297},

abstract = {This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT–UMAP combination stands out in the evaluation metrics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{suhn_vibro-acoustic_2023,

title = {Vibro-Acoustic Sensing of Instrument Interactions as a Potential Source of Texture-Related Information in Robotic Palpation},

author = {T Sühn and N Esmaeili and S Mattepu and M Spiller and A Boese and R Urrutia and V Poblete and C Hansen and C Lohmann and A Illanes and M Friebe},

url = {https://www.mdpi.com/1424-8220/23/6/3141

https://pubmed.ncbi.nlm.nih.gov/36991854/, PubMed},

doi = {10.3390/s23063141},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Sensors},

volume = {23},

number = {6},

pages = {3141},

abstract = {The direct tactile assessment of surface textures during palpation is an essential component of open surgery that is impeded in minimally invasive and robot-assisted surgery. When indirectly palpating with a surgical instrument, the structural vibrations from this interaction contain tactile information that can be extracted and analysed. This study investigates the influence of the parameters contact angle α and velocity v→ on the vibro-acoustic signals from this indirect palpation. A 7-DOF robotic arm, a standard surgical instrument, and a vibration measurement system were used to palpate three different materials with varying α and v→. The signals were processed based on continuous wavelet transformation. They showed material-specific signatures in the time–frequency domain that retained their general characteristic for varying α and v→. Energy-related and statistical features were extracted, and supervised classification was performed, where the testing data comprised only signals acquired with different palpation parameters than for training data. The classifiers support vector machine and k-nearest neighbours provided 99.67% and 96.00% accuracy for the differentiation of the materials. The results indicate the robustness of the features against variations in the palpation parameters. This is a prerequisite for an application in minimally invasive surgery but needs to be confirmed in realistic experiments with biological tissues.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gumbs_2022,

title = {The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature},

author = {AA Gumbs and V Grasso and N Bourdel and R Croner and G Spolverato and I Frigerio and A Illanes and MA Hilal and A Park and E Elyan},

url = {http://dx.doi.org/10.3390/s22134918

https://pubmed.ncbi.nlm.nih.gov/35808408/, PubMed},

doi = {10.3390/s22134918},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

journal = {Sensors},

volume = {22},

number = {13},

pages = {4918},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Esmaeili_2021,

title = {Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging},

author = {N Esmaeili and E Sharaf and EJG Ataide and A Illanes and A Boese and N Davaris and C Arens and N Navab and M Friebe},

url = {http://dx.doi.org/10.3390/s21238157

https://pubmed.ncbi.nlm.nih.gov/34884166/, PubMed},

doi = {10.3390/s21238157},

year  = {2021},

date = {2021-12-01},

urldate = {2021-12-01},

journal = {Sensors},

volume = {21},

number = {23},

pages = {8157},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sabieleish_study_2021,

title = {Study of Needle Punctures into Soft Tissue Through Audio and Force Sensing: Can Audio Be a Simple Alternative for Needle Guidance?},

author = {M Sabieleish and K Heryan and A Boese and C Hansen and M Friebe and A Illanes},

url = {https://link.springer.com/10.1007/s11548-021-02479-x

https://pubmed.ncbi.nlm.nih.gov/34652603/, PubMed},

doi = {10.1007/s11548-021-02479-x},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

journal = {International Journal of Computer Assisted Radiology and Surgery},

volume = {16},

number = {10},

pages = {1683–1697},

abstract = {Purpose Percutaneous needle insertion is one of the most common minimally invasive procedures. The clinician’s experience and medical imaging support are essential to the procedure’s safety. However, imaging comes with inaccuracies due to artifacts, and therefore sensor-based solutions were proposed to improve accuracy. However, sensors are usually embedded in the needle tip, leading to design limitations. A novel concept was proposed for capturing tip–tissue interaction information through audio sensing, showing promising results for needle guidance. This work demonstrates that this audio approach can provide important puncture information by comparing audio and force signal dynamics during insertion. 

Methods An experimental setup for inserting a needle into soft tissue was prepared. Audio and force signals were synchronously recorded at four different insertion velocities, and a dataset of 200 recordings was acquired. Indicators related to different aspects of the force and audio were compared through signal-to-signal and event-to-event correlation analysis. 

Results High signal-to-signal correlations between force and audio indicators regardless of the insertion velocity were obtained. The force curvature indicator obtained the best correlation performances to audio with more than 70% of the correlations higher than 0.6. The event-to-event correlation analysis shows that a puncture event in the force is generally identifiable in audio and that their intensities firmly related. 

Conclusions Audio contains valuable information for monitoring needle tip/tissue interaction. Significant dynamics obtained from a well-known sensor as force can also be extracted from audio, regardless of insertion velocities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Salvi_2021,

title = {Vascular Auscultation of Carotid Artery: Towards Biometric Identification and Verification of Individuals},

author = {R Salvi and P Fuentealba and J Henze and P Bisgin and T Sühn and M Spiller and A Burmann and A Boese and A Illanes and M Friebe},

url = {http://dx.doi.org/10.3390/s21196656},

doi = {10.3390/s21196656},

year  = {2021},

date = {2021-10-01},

urldate = {2021-10-01},

journal = {Sensors},

volume = {21},

number = {19},

pages = {6656},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gumbs_2021,

title = {Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?},

author = {AA Gumbs and I Frigerio and G Spolverato and R Croner and A Illanes and E Chouillard and E Elyan},

url = {http://dx.doi.org/10.3390/s21165526},

doi = {10.3390/s21165526},

year  = {2021},

date = {2021-08-01},

urldate = {2021-08-01},

journal = {Sensors},

volume = {21},

number = {16},

pages = {5526},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Esmaeili_2021_2,

title = {Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy—Narrow Band Imaging},

author = {N Esmaeili and A Boese and N Davaris and C Arens and N Navab and M Friebe and A Illanes},

url = {http://dx.doi.org/10.3390/diagnostics11030432},

doi = {10.3390/diagnostics11030432},

year  = {2021},

date = {2021-03-01},

urldate = {2021-03-01},

journal = {Diagnostics},

volume = {11},

number = {3},

pages = {432},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{S_hn_2020,

title = {Auscultation System for Acquisition of Vascular Sounds – Towards Sound-Based Monitoring of the Carotid Artery},

author = {T Sühn and M Spiller and R Salvi and S Hellwig and A Boese and A Illanes and M Friebe},

url = {http://dx.doi.org/10.2147/MDER.S268057

https://pubmed.ncbi.nlm.nih.gov/33162758/, PubMed},

doi = {10.2147/mder.s268057},

year  = {2020},

date = {2020-10-01},

urldate = {2020-10-01},

journal = {Medical Devices: Evidence and Research},

volume = {Volume 13},

pages = {349–364},

publisher = {Informa UK Limited},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gomes_Ataide_2020,

title = {Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features},

author = {EJG Ataide and N Ponugoti and A Illanes and S Schenke and M Kreissl and M Friebe},

url = {http://dx.doi.org/10.3390/s20216110

https://pubmed.ncbi.nlm.nih.gov/33121054/, PubMed},

doi = {10.3390/s20216110},

year  = {2020},

date = {2020-10-01},

urldate = {2020-10-01},

journal = {Sensors},

volume = {20},

number = {21},

pages = {6110},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{S_hn_2020_2,

title = {Acoustic sensing of tissue-tool interactions – potential applications in arthroscopic surgery},

author = {T Sühn and A Pandey and M Friebe and A Illanes and A Boese and C Lohmann},

url = {http://dx.doi.org/10.1515/cdbme-2020-3152},

doi = {10.1515/cdbme-2020-3152},

year  = {2020},

date = {2020-09-01},

urldate = {2020-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {6},

number = {3},

pages = {595–598},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fuentealba_2020,

title = {Investigating an Optimal Signal Epoch Length for Cardiotocographic Classification},

author = {P Fuentealba and A Illanes and F Ortmeier and P Poudel},

url = {http://dx.doi.org/10.1515/cdbme-2020-3132},

doi = {10.1515/cdbme-2020-3132},

year  = {2020},

date = {2020-09-01},

urldate = {2020-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {6},

number = {3},

pages = {514–517},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Esmaeili_2020,

title = {Manual versus Automatic Classification of Laryngeal Lesions based on Vascular Patterns in CE+NBI Images},

author = {N Esmaeili and A Illanes and A Boese and N Davaris and C Arens and N Navab and M Friebe},

url = {http://dx.doi.org/10.1515/cdbme-2020-3018},

doi = {10.1515/cdbme-2020-3018},

year  = {2020},

date = {2020-09-01},

urldate = {2020-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {6},

number = {3},

pages = {70–73},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schaufler_2020,

title = {Surgical Audio Guidance: Feasibility Check for Robotic Surgery Procedures},

author = {A Schaufler and A Illanes and I Maldonado and A Boese and R Croner and M Friebe},

url = {http://dx.doi.org/10.1515/cdbme-2020-3146},

doi = {10.1515/cdbme-2020-3146},

year  = {2020},

date = {2020-09-01},

urldate = {2020-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {6},

number = {3},

pages = {571–574},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Kewlani_2020,

title = {Integration of Acoustic Emission and Ultrasound for Needle Guidance in Interventional Procedures},

author = {L Kewlani and A Illanes and B Menze and M Friebe},

url = {http://dx.doi.org/10.4018/ijbce.2020070104},

doi = {10.4018/ijbce.2020070104},

year  = {2020},

date = {2020-07-01},

urldate = {2020-07-01},

journal = {International Journal of Biomedical and Clinical Engineering},

volume = {9},

number = {2},

pages = {45–55},

publisher = {IGI Global Scientific Publishing},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Esmaeili_2020_2,

title = {Laryngeal Lesion Classification Based on Vascular Patterns in Contact Endoscopy and Narrow Band Imaging: Manual Versus Automatic Approach},

author = {N Esmaeili and A Illanes and A Boese and N Davaris and C Arens and N Navab and M Friebe},

url = {http://dx.doi.org/10.3390/s20144018

https://pubmed.ncbi.nlm.nih.gov/32707740/, PubMed},

doi = {10.3390/s20144018},

year  = {2020},

date = {2020-07-01},

urldate = {2020-07-01},

journal = {Sensors},

volume = {20},

number = {14},

pages = {4018},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Boese_2020,

title = {Endoscopic filter fluorometer for detection of accumulation of Protoporphyrin IX to improve photodynamic diagnostic (PDD)},

author = {A Boese and A Wagner and A Illanes and UB Liehr and JJ Wendler and M Friebe},

url = {http://dx.doi.org/10.1515/cdbme-2020-0018},

doi = {10.1515/cdbme-2020-0018},

year  = {2020},

date = {2020-05-01},

urldate = {2020-05-01},

journal = {Current Directions in Biomedical Engineering},

volume = {6},

number = {1},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ziegle_2020,

title = {Frequency and average gray-level information for thermal ablation status in ultrasound B-Mode sequences},

author = {J Ziegle and A Illanes and A Boese and M Friebe},

url = {http://dx.doi.org/10.1515/cdbme-2020-0023},

doi = {10.1515/cdbme-2020-0023},

year  = {2020},

date = {2020-05-01},

urldate = {2020-05-01},

journal = {Current Directions in Biomedical Engineering},

volume = {6},

number = {1},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Illanes_2020,

title = {Surgical audio information as base for haptic feedback in robotic-assisted procedures},

author = {A Illanes and A Schaufler and T Sühn and A Boese and R Croner and M Friebe},

url = {http://dx.doi.org/10.1515/cdbme-2020-0036},

doi = {10.1515/cdbme-2020-0036},

year  = {2020},

date = {2020-05-01},

urldate = {2020-05-01},

journal = {Current Directions in Biomedical Engineering},

volume = {6},

number = {1},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Davaris_2020,

title = {Evaluation of Vascular Patterns Using Contact Endoscopy and Narrow-Band Imaging (CE-NBI) for the Diagnosis of Vocal Fold Malignancy},

author = {N Davaris and A Lux and N Esmaeili and A Illanes and A Boese and M Friebe and C Arens},

url = {http://dx.doi.org/10.3390/cancers12010248

https://pubmed.ncbi.nlm.nih.gov/31968528/, PubMed},

doi = {10.3390/cancers12010248},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Cancers},

volume = {12},

number = {1},

pages = {248},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fuentealba_2019,

title = {Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment},

author = {P Fuentealba and A Illanes and F Ortmeier},

url = {http://dx.doi.org/10.3390/app9245421},

doi = {10.3390/app9245421},

year  = {2019},

date = {2019-12-01},

urldate = {2019-12-01},

journal = {Applied Sciences},

volume = {9},

number = {24},

pages = {5421},

publisher = {MDPI AG},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mahmoodian_2019_3,

title = {Seizure prediction with cross-higher-order spectral analysis of EEG signals},

author = {N Mahmoodian and J Haddadnia and A Illanes and A Boese and M Friebe},

url = {http://dx.doi.org/10.1007/s11760-019-01615-0},

doi = {10.1007/s11760-019-01615-0},

year  = {2019},

date = {2019-12-01},

urldate = {2019-12-01},

journal = {Signal, Image and Video Processing},

volume = {14},

number = {4},

pages = {821–828},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Maldonado_2019,

title = {Audio waves and its loss of energy in puncture needles},

author = {I Maldonado and A Illanes and M Kalmar and T Sühn and A Boese and M Friebe},

url = {http://dx.doi.org/10.1515/cdbme-2019-0006},

doi = {10.1515/cdbme-2019-0006},

year  = {2019},

date = {2019-09-01},

urldate = {2019-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {5},

number = {1},

pages = {21–24},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Schaufler_2019,

title = {Automatic differentiation between Veress needle events in laparoscopic access using proximally attached audio signal characterization},

author = {A Schaufler and T Sühn and N Esmaeili and A Boese and C Wex and R Croner and M Friebe and A Illanes},

url = {http://dx.doi.org/10.1515/cdbme-2019-0093},

doi = {10.1515/cdbme-2019-0093},

year  = {2019},

date = {2019-09-01},

urldate = {2019-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {5},

number = {1},

pages = {369–371},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{S_hn_2019,

title = {Computer Assisted Auscultation System for Phonoangiography of the Carotid Artery},

author = {T Sühn and N Mahmoodian and A Sreenivas and I Maldonado and M Spiller and A Boese and A Illanes and M Friebe and M Bloxton},

url = {http://dx.doi.org/10.1515/cdbme-2019-0044},

doi = {10.1515/cdbme-2019-0044},

year  = {2019},

date = {2019-09-01},

urldate = {2019-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {5},

number = {1},

pages = {175–178},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fuentealba_2019_2,

title = {Foetal heart rate assessment by empirical mode decomposition and spectral analysis},

author = {P Fuentealba and A Illanes and F Ortmeier},

url = {http://dx.doi.org/10.1515/cdbme-2019-0096},

doi = {10.1515/cdbme-2019-0096},

year  = {2019},

date = {2019-09-01},

urldate = {2019-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {5},

number = {1},

pages = {381–383},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chen_2019,

title = {Texture differentiation using audio signal analysis with robotic interventional instruments},

author = {CH Chen and T Sühn and M Kalmar and I Maldonado and C Wex and R Croner and A Boese and M Friebe and A Illanes},

url = {http://dx.doi.org/10.1016/j.compbiomed.2019.103370

https://pubmed.ncbi.nlm.nih.gov/31374348/, PubMed},

doi = {10.1016/j.compbiomed.2019.103370},

year  = {2019},

date = {2019-09-01},

urldate = {2019-09-01},

journal = {Computers in Biology and Medicine},

volume = {112},

pages = {103370},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Esmaeili_2019_2,

title = {Novel automated vessel pattern characterization of larynx contact endoscopic video images},

author = {N Esmaeili and A Illanes and A Boese and N Davaris and C Arens and M Friebe},

url = {http://dx.doi.org/10.1007/s11548-019-02034-9

https://pubmed.ncbi.nlm.nih.gov/31352673/, PubMed},

doi = {10.1007/s11548-019-02034-9},

year  = {2019},

date = {2019-07-01},

urldate = {2019-07-01},

journal = {International Journal of Computer Assisted Radiology and Surgery},

volume = {14},

number = {10},

pages = {1751–1761},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{China_2019,

title = {Anatomical Structure Segmentation in Ultrasound Volumes Using Cross Frame Belief Propagating Iterative Random Walks},

author = {D China and A Illanes and P Poudel and M Friebe and P Mitra and D Sheet},

url = {http://dx.doi.org/10.1109/JBHI.2018.2864896

https://pubmed.ncbi.nlm.nih.gov/30113902/, PubMed},

doi = {10.1109/jbhi.2018.2864896},

year  = {2019},

date = {2019-05-01},

urldate = {2019-05-01},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {23},

number = {3},

pages = {1110–1118},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mahmoodian_2019_2,

title = {Proximal detection of guide wire perforation using feature extraction from bispectral audio signal analysis combined with machine learning},

author = {N Mahmoodian and A Schaufler and A Pashazadeh and A Boese and M Friebe and A Illanes},

url = {http://dx.doi.org/10.1016/j.compbiomed.2019.02.001

https://pubmed.ncbi.nlm.nih.gov/30769168/, PubMed},

doi = {10.1016/j.compbiomed.2019.02.001},

year  = {2019},

date = {2019-04-01},

urldate = {2019-04-01},

journal = {Computers in Biology and Medicine},

volume = {107},

pages = {10–17},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fuentealba_2019_4,

title = {Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment},

author = {P Fuentealba and A Illanes and F Ortmeier},

url = {http://dx.doi.org/10.1109/access.2019.2950798},

doi = {10.1109/access.2019.2950798},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {IEEE Access},

volume = {7},

pages = {159754–159772},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Illanes_2019_2,

title = {Parametrical modelling for texture characterization—A novel approach applied to ultrasound thyroid segmentation},

author = {A Illanes and N Esmaeili and P Poudel and S Balakrishnan and M Friebe},

editor = {W Speier},

url = {http://dx.doi.org/10.1371/journal.pone.0211215

https://pubmed.ncbi.nlm.nih.gov/30695052/, PubMed},

doi = {10.1371/journal.pone.0211215},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {PLOS ONE},

volume = {14},

number = {1},

pages = {e0211215},

publisher = {Public Library of Science (PLoS)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Poudel_2019_2,

title = {Thyroid Ultrasound Texture Classification Using Autoregressive Features in Conjunction With Machine Learning Approaches},

author = {P Poudel and A Illanes and EJG Ataide and N Esmaeili and S Balakrishnan and M Friebe},

url = {http://dx.doi.org/10.1109/access.2019.2923547},

doi = {10.1109/access.2019.2923547},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {IEEE Access},

volume = {7},

pages = {79354–79365},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Poudel_2018,

title = {Evaluation of Commonly Used Algorithms for Thyroid Ultrasound Images Segmentation and Improvement Using Machine Learning Approaches},

author = {P Poudel and A Illanes and D Sheet and M Friebe},

url = {http://dx.doi.org/10.1155/2018/8087624

https://pubmed.ncbi.nlm.nih.gov/30344990/, PubMed},

doi = {10.1155/2018/8087624},

year  = {2018},

date = {2018-09-01},

urldate = {2018-09-01},

journal = {Journal of Healthcare Engineering},

volume = {2018},

pages = {1–13},

publisher = {Wiley},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fuentealba_2018,

title = {Foetal heart rate signal spectral analysis by using time-varying autoregressive modelling},

author = {P Fuentealba and A Illanes and F Ortmeier},

url = {http://dx.doi.org/10.1515/cdbme-2018-0139},

doi = {10.1515/cdbme-2018-0139},

year  = {2018},

date = {2018-09-01},

urldate = {2018-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {4},

number = {1},

pages = {579–582},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chen_2018,

title = {Proximally placed signal acquisition sensoric for robotic tissue tool interactions},

author = {CH Chen and T Sühn and A Illanes and I Maldonado and H Ahmad and C Wex and R Croner and A Boese and M Friebe},

url = {http://dx.doi.org/10.1515/cdbme-2018-0017},

doi = {10.1515/cdbme-2018-0017},

year  = {2018},

date = {2018-09-01},

urldate = {2018-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {4},

number = {1},

pages = {67–70},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Boese_2018,

title = {Vascular pattern detection and recognition in endoscopic imaging of the vocal folds},

author = {A Boese and A Illanes and S Balakrishnan and N Davaris and C Arens and M Friebe},

url = {http://dx.doi.org/10.1515/cdbme-2018-0019},

doi = {10.1515/cdbme-2018-0019},

year  = {2018},

date = {2018-09-01},

urldate = {2018-09-01},

journal = {Current Directions in Biomedical Engineering},

volume = {4},

number = {1},

pages = {75–78},

publisher = {Walter de Gruyter GmbH},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Landes_2018,

title = {A study of concentration changes of Protoporphyrin IX and Coproporphyrin III in mixed samples mimicking conditions inside cancer cells for Photodynamic Therapy},

author = {R Landes and A Illanes and D Goeppner and H Gollnick and M Friebe},

editor = {MR Hamblin},

url = {http://dx.doi.org/10.1371/journal.pone.0202349},

doi = {10.1371/journal.pone.0202349},

year  = {2018},

date = {2018-08-01},

urldate = {2018-08-01},

journal = {PLOS ONE},

volume = {13},

number = {8},

pages = {e0202349},

publisher = {Public Library of Science (PLoS)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}