TY - JOUR A1 - Pereira, Clayton R. A1 - Pereira, Danilo R. A1 - Silva, Francisco A. A1 - Masieiro, Joao P. A1 - Weber, Silke A. T. A1 - Hook, Christian A1 - Papa, João Paulo T1 - A new computer vision-based approach to aid the diagnosis of Parkinson's disease JF - Computer Methods and Programs in Biomedicine N2 - Background and Objective: Even today, pointing out an exam that can diagnose a patient with Parkinson's disease (PD) accurately enough is not an easy task. Although a number of techniques have been used in search for a more precise method, detecting such illness and measuring its level of severity early enough to postpone its side effects are not straightforward. In this work, after reviewing a considerable number of works, we conclude that only a few techniques address the problem of PD recognition by means of micrography using computer vision techniques. Therefore, we consider the problem of aiding automatic PD diagnosis by means of spirals and meanders filled out in forms, which are then compared with the template for feature extraction. Methods: In our work, both the template and the drawings are identified and separated automatically using image processing techniques, thus needing no user intervention. Since we have no registered images, the idea is to obtain a suitable representation of both template and drawings using the very same approach for all images in a fast and accurate approach. Results: The results have shown that we can obtain very reasonable recognition rates (around approximate to 67%), with the most accurate class being the one represented by the patients, which outnumbered the control individuals in the proposed dataset. Conclusions: The proposed approach seemed to be suitable for aiding in automatic PD diagnosis by means of computer vision and machine learning techniques. Also, meander images play an important role, leading to higher accuracies than spiral images. We also observed that the main problem in detecting PD is the patients in the early stages, who can draw near-perfect objects, which are very similar to the ones made by control patients. (C) 2016 Elsevier Ireland Ltd. All rights reserved. KW - CLASSIFICATION KW - Micrography KW - Parkinson's disease KW - Pattern recognition KW - SPEECH Y1 - 2016 U6 - https://doi.org/10.1016/j.cmpb.2016.08.005 VL - 136 SP - 79 EP - 88 PB - Elsevier ER - TY - INPR A1 - Rückert, Tobias A1 - Rauber, David A1 - Maerkl, Raphaela A1 - Klausmann, Leonard A1 - Yildiran, Suemeyye R. A1 - Gutbrod, Max A1 - Nunes, Danilo Weber A1 - Moreno, Alvaro Fernandez A1 - Luengo, Imanol A1 - Stoyanov, Danail A1 - Toussaint, Nicolas A1 - Cho, Enki A1 - Kim, Hyeon Bae A1 - Choo, Oh Sung A1 - Kim, Ka Young A1 - Kim, Seong Tae A1 - Arantes, Gonçalo A1 - Song, Kehan A1 - Zhu, Jianjun A1 - Xiong, Junchen A1 - Lin, Tingyi A1 - Kikuchi, Shunsuke A1 - Matsuzaki, Hiroki A1 - Kouno, Atsushi A1 - Manesco, João Renato Ribeiro A1 - Papa, João Paulo A1 - Choi, Tae-Min A1 - Jeong, Tae Kyeong A1 - Park, Juyoun A1 - Alabi, Oluwatosin A1 - Wei, Meng A1 - Vercauteren, Tom A1 - Wu, Runzhi A1 - Xu, Mengya A1 - an Wang, A1 - Bai, Long A1 - Ren, Hongliang A1 - Yamlahi, Amine A1 - Hennighausen, Jakob A1 - Maier-Hein, Lena A1 - Kondo, Satoshi A1 - Kasai, Satoshi A1 - Hirasawa, Kousuke A1 - Yang, Shu A1 - Wang, Yihui A1 - Chen, Hao A1 - Rodríguez, Santiago A1 - Aparicio, Nicolás A1 - Manrique, Leonardo A1 - Lyons, Juan Camilo A1 - Hosie, Olivia A1 - Ayobi, Nicolás A1 - Arbeláez, Pablo A1 - Li, Yiping A1 - Khalil, Yasmina Al A1 - Nasirihaghighi, Sahar A1 - Speidel, Stefanie A1 - Rückert, Daniel A1 - Feussner, Hubertus A1 - Wilhelm, Dirk A1 - Palm, Christoph T1 - Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge N2 - Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context - such as the current procedural phase - has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding. Y1 - 2025 N1 - Der Aufsatz wurde peer-reviewed veröffentlicht und ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/start/0/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/10.1016%2Fj.media.2026.103945/docId/8846 ER - TY - JOUR A1 - Rueckert, Tobias A1 - Rauber, David A1 - Maerkl, Raphaela A1 - Klausmann, Leonard A1 - Yildiran, Suemeyye R. A1 - Gutbrod, Max A1 - Nunes, Danilo Weber A1 - Moreno, Alvaro Fernandez A1 - Luengo, Imanol A1 - Stoyanov, Danail A1 - Toussaint, Nicolas A1 - Cho, Enki A1 - Kim, Hyeon Bae A1 - Choo, Oh Sung A1 - Kim, Ka Young A1 - Kim, Seong Tae A1 - Arantes, Gonçalo A1 - Song, Kehan A1 - Zhu, Jianjun A1 - Xiong, Junchen A1 - Lin, Tingyi A1 - Kikuchi, Shunsuke A1 - Matsuzaki, Hiroki A1 - Kouno, Atsushi A1 - Manesco, João Renato Ribeiro A1 - Papa, João Paulo A1 - Choi, Tae-Min A1 - Jeong, Tae Kyeong A1 - Park, Juyoun A1 - Alabi, Oluwatosin A1 - Wei, Meng A1 - Vercauteren, Tom A1 - Wu, Runzhi A1 - Xu, Mengya A1 - Wang, An A1 - Bai, Long A1 - Ren, Hongliang A1 - Yamlahi, Amine A1 - Hennighausen, Jakob A1 - Maier-Hein, Lena A1 - Kondo, Satoshi A1 - Kasai, Satoshi A1 - Hirasawa, Kousuke A1 - Yang, Shu A1 - Wang, Yihui A1 - Chen, Hao A1 - Rodríguez, Santiago A1 - Aparicio, Nicolás A1 - Manrique, Leonardo A1 - Palm, Christoph A1 - Wilhelm, Dirk A1 - Feussner, Hubertus A1 - Rueckert, Daniel A1 - Speidel, Stefanie A1 - Nasirihaghighi, Sahar A1 - Al Khalil, Yasmina A1 - Li, Yiping A1 - Arbeláez, Pablo A1 - Ayobi, Nicolás A1 - Hosie, Olivia A1 - Lyons, Juan Camilo T1 - Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge JF - Medical Image Analysis N2 - Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context – such as the current procedural phase – has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding. KW - Surgical phase recognition KW - Instrument keypoint estimation KW - Instrument instance segmentation KW - Robot-assisted surgery Y1 - 2026 U6 - https://doi.org/10.1016/j.media.2026.103945 SN - 1361-8415 N1 - Corresponding author der OTH Regensburg: Tobias Rueckert Die Preprint-Version ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/solrsearch/index/search/start/0/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/2507.16559 VL - 109 PB - Elsevier ER - TY - CHAP A1 - Pereira, Clayton R. A1 - Passos, Leandro A. A1 - Lopes, Ricardo R. A1 - Weber, Silke A. T. A1 - Hook, Christian A1 - Papa, Joao Paulo T1 - Parkinson’s Disease Identification Using Restricted Boltzmann Machines T2 - Computer Analysis of Images and Patterns, 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part II N2 - Currently, Parkinson’s Disease (PD) has no cure or accurate diagnosis, reaching approximately 60, 000 new cases yearly and worldwide, being more often in the elderly population. Its main symptoms can not be easily uncorrelated with other illness, being way more difficult to be identified at the early stages. As such, computer-aided tools have been recently used to assist in this task, but the challenge in the automatic identification of Parkinson’s Disease still persists. In order to cope with this problem, we propose to employ Restricted Boltzmann Machines (RBMs) to learn features in an unsupervised fashion by analyzing images from handwriting exams, which aim at assessing the writing skills of potential individuals. These are one of the main symptoms of PD-prone people, since such kind of ability ends up being severely affected. We show that RBMs can learn proper features that help supervised classifiers in the task of automatic identification of PD patients, as well as one can obtain a more compact representation of the exam for the sake of storage and computational load purposes. KW - Bevölkerung KW - Handschrift KW - maschinelles Lernen Y1 - 2017 SN - 978-3-319-64697-8 U6 - https://doi.org/10.1007/978-3-319-64698-5_7 VL - 10425 SP - 70 EP - 80 PB - Springer ER - TY - JOUR A1 - Pereira, Clayton R. A1 - Pereira, Danilo R. A1 - Rosa, Gustavo H. A1 - Albuquerque, Victor Hugo C. A1 - Weber, Silke A. T. A1 - Hook, Christian A1 - Papa, João Paulo T1 - Handwritten dynamics assessment through convolutional neural networks BT - An application to Parkinson’s disease identification JF - Artificial Intelligence in Medicine N2 - Background and objective Parkinson’s disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. Methods In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual’s assessment. Additionally, we make available a dataset composed of images and signal-based data to foster the research related to computer-aided PD diagnosis. Results The proposed approach was compared against raw data and texture-based descriptors, showing suitable results, mainly in the context of early stage detection, with results nearly to 95%. Conclusions The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson’s disease identification, as well as it can outperform handcrafted features. KW - Convolutional neural networks KW - Handwritten dynamics KW - Parkinson’s disease Y1 - 2018 U6 - https://doi.org/10.1016/j.artmed.2018.04.001 VL - 37 IS - May SP - 67 EP - 77 PB - Elsevier ER - TY - JOUR A1 - Afonso, Luis Claudio Sugi A1 - Rosa, Gustavo H. A1 - Pereira, Clayton R. A1 - Weber, Silke A. T. A1 - Hook, Christian A1 - Albuquerque, Victor Hugo C. A1 - Papa, Joao Paulo T1 - A recurrence plot-based approach for Parkinson's disease identification JF - Future generation computer systems - The international journal of escience N2 - Parkinson's disease (PD) is a neurodegenerative disease that affects millions of people worldwide, causing mental and mainly motor dysfunctions. The negative impact on the patient's daily routine has moved the science in search of new techniques that can reduce its negative effects and also identify the disease in individuals. One of the main motor characteristics of PD is the hand tremor faced by patients, which turns out to be a crucial information to be used towards a computer-aided diagnosis. In this context, we make use of handwriting dynamics data acquired from individuals when submitted to some tasks that measure abilities related to writing skills. This work proposes the application of recurrence plots to map the signals onto the image domain, which are further used to feed a Convolutional Neural Network for learning proper information that can help the automatic identification of PD. The proposed approach was assessed in a public dataset under several scenarios that comprise different combinations of deep-based architectures, image resolutions, and training set sizes. Experimental results showed significant accuracy improvement compared to our previous work with an average accuracy of over 87%. Moreover, it was observed an improvement in accuracy concerning the classification of patients (i.e., mean recognition rates above to 90%). The promising results showed the potential of the proposed approach towards the automatic identification of Parkinson's disease. KW - Classification KW - Convolutional neural networks KW - diagnosis KW - Optimum-path forest KW - Parkinson's disease KW - Recurrence plot Y1 - 2019 U6 - https://doi.org/10.1016/j.future.2018.11.054 VL - 94 IS - May SP - 282 EP - 292 PB - Elsevier ER - TY - JOUR A1 - Afonso, Luis Claudio Sugi A1 - Pereira, Clayton R. A1 - Weber, Silke A. T. A1 - Hook, Christian A1 - Falcão, Alexandre X. A1 - Papa, Joao Paulo T1 - Hierarchical learning using deep optimum-path forest JF - Journal of Visual Communication and Image Representation N2 - Bag-of-Visual Words (BoVW) and deep learning techniques have been widely used in several domains, which include computer-assisted medical diagnoses. In this work, we are interested in developing tools for the automatic identification of Parkinson’s disease using machine learning and the concept of BoVW. The proposed approach concerns a hierarchical-based learning technique to design visual dictionaries through the Deep Optimum-Path Forest classifier. The proposed method was evaluated in six datasets derived from data collected from individuals when performing handwriting exams. Experimental results showed the potential of the technique, with robust achievements. KW - Handwriting dynamics KW - Hierarchical representation KW - Optimum-path forest KW - Parkinson’s disease Y1 - 2020 U6 - https://doi.org/10.1016/j.jvcir.2020.102823 VL - 71 IS - August PB - Elsevier ER - TY - CHAP A1 - Pereira, Clayton R. A1 - Pereira, Danilo R. A1 - da Silva, Francisco A. A1 - Hook, Christian A1 - Weber, Silke A. T. A1 - Pereira, Luis A. M. A1 - Papa, João Paulo T1 - A Step Towards the Automated Diagnosis of Parkinson's Disease: Analyzing Handwriting Movements T2 - 2015 IEEE 28th International Symposium on Computer-Based Medical Systems, 22.-25.06.2015, Sao Carlos, Brazil N2 - Parkinson’s disease (PD) has affected millions of people world-wide, being its major problem the loss of movements and, consequently, the ability of working and locomotion. Although we can find several works that attempt at dealing with this problem out there, most of them make use of datasets composed by a few subjects only. In this work, we present some results toward the automated diagnosis of PD by means of computer vision-based techniques in a dataset composed by dozens of patients, which is one of the main contributions of this work. The dataset is part of a joint research project that aims at extracting both visual and signal-based information from healthy and PD patients in order to go forward the early diagnosis of PD patients. The dataset is composed by handwriting clinical exams that are analyzed by means of image processing and machine learning techniques, being the preliminary results encouraging and promising. Additionally, a new quantitative feature to measure the amount of tremor of an individual’s handwritten trace called Mean Relative Tremor is also presented. Y1 - 2015 SN - 978-1-4673-6775-2 U6 - https://doi.org/10.1109/cbms.2015.34 SN - 2372-9198 SP - 171 EP - 176 PB - IEEE ER - TY - JOUR A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Scheppach, Markus W. A1 - Probst, Andreas A1 - Shahidi, Neal A1 - Prinz, Friederike A1 - Fleischmann, Carola A1 - Römmele, Christoph A1 - Gölder, Stefan Karl A1 - Braun, Georg A1 - Rauber, David A1 - Rückert, Tobias A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Byrne, Michael F. A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm JF - Gut N2 - In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training. KW - Artificial Intelligence KW - Endoscopy KW - Medical Image Computing Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-54293 VL - 71 IS - 12 SP - 2388 EP - 2390 PB - BMJ CY - London ER - TY - JOUR A1 - Souza Jr., Luis Antonio de A1 - Palm, Christoph A1 - Mendel, Robert A1 - Hook, Christian A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Weber, Silke A. T. A1 - Papa, João Paulo T1 - A survey on Barrett's esophagus analysis using machine learning JF - Computers in Biology and Medicine N2 - This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods. KW - Speiseröhrenkrankheit KW - Diagnose KW - Mustererkennung KW - Maschinelles Lernen KW - Literaturbericht KW - Barrett's esophagus KW - Machine learning KW - Adenocarcinoma KW - Image processing KW - Pattern recognition KW - Computer-aided diagnosis Y1 - 2018 U6 - https://doi.org/10.1016/j.compbiomed.2018.03.014 VL - 96 SP - 203 EP - 213 PB - Elsevier ER -