@article{MaierWeihererHuberetal., author = {Maier, Johannes and Weiherer, Maximilian and Huber, Michaela and Palm, Christoph}, title = {Imitating human soft tissue on basis of a dual-material 3D print using a support-filled metamaterial to provide bimanual haptic for a hand surgery training system}, series = {Quantitative Imaging in Medicine and Surgery}, volume = {9}, journal = {Quantitative Imaging in Medicine and Surgery}, number = {1}, publisher = {AME Publishing Company}, doi = {10.21037/qims.2018.09.17}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-979}, pages = {30 -- 42}, abstract = {Background: Currently, it is common practice to use three-dimensional (3D) printers not only for rapid prototyping in the industry, but also in the medical area to create medical applications for training inexperienced surgeons. In a clinical training simulator for minimally invasive bone drilling to fix hand fractures with Kirschner-wires (K-wires), a 3D-printed hand phantom must not only be geometrically but also haptically correct. Due to a limited view during an operation, surgeons need to perfectly localize underlying risk structures only by feeling of specific bony protrusions of the human hand. Methods: The goal of this experiment is to imitate human soft tissue with its haptic and elasticity for a realistic hand phantom fabrication, using only a dual-material 3D printer and support-material-filled metamaterial between skin and bone. We present our workflow to generate lattice structures between hard bone and soft skin with iterative cube edge (CE) or cube face (CF) unit cells. Cuboid and finger shaped sample prints with and without inner hard bone in different lattice thickness are constructed and 3D printed. Results: The most elastic available rubber-like material is too firm to imitate soft tissue. By reducing the amount of rubber in the inner volume through support material (SUP), objects become significantly softer. Without metamaterial, after disintegration, the SUP can be shifted through the volume and thus the body loses its original shape. Although the CE design increases the elasticity, it cannot restore the fabric form. In contrast to CE, the CF design increases not only the elasticity but also guarantees a local limitation of the SUP. Therefore, the body retains its shape and internal bones remain in its intended place. Various unit cell sizes, lattice thickening and skin thickness regulate the rubber material and SUP ratio. Test prints with higher SUP and lower rubber material percentage appear softer and vice versa. This was confirmed by an expert surgeon evaluation. Subjects adjudged pure rubber-like material as too firm and samples only filled with SUP or lattice structure in CE design as not suitable for imitating tissue. 3D-printed finger samples in CF design were rated as realistic compared to the haptic of human tissue with a good palpable bone structure. Conclusions: We developed a new dual-material 3D print technique to imitate soft tissue of the human hand with its haptic properties. Blowy SUP is trapped within a lattice structure to soften rubber-like 3D print material, which makes it possible to reproduce a realistic replica of human hand soft tissue.}, subject = {Handchirurgie}, language = {en} } @misc{MaierWeihererHuberetal., author = {Maier, Johannes and Weiherer, Maximilian and Huber, Michaela and Palm, Christoph}, title = {Abstract: Imitating Human Soft Tissue with Dual-Material 3D Printing}, series = {Bildverarbeitung f{\"u}r die Medizin 2019, Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. M{\"a}rz 2019 in L{\"u}beck}, journal = {Bildverarbeitung f{\"u}r die Medizin 2019, Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. M{\"a}rz 2019 in L{\"u}beck}, editor = {Handels, Heinz and Deserno, Thomas M. and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-25325-7}, doi = {10.1007/978-3-658-25326-4_48}, pages = {218}, abstract = {Currently, it is common practice to use three-dimensional (3D) printers not only for rapid prototyping in the industry, but also in the medical area to create medical applications for training inexperienced surgeons. In a clinical training simulator for minimally invasive bone drilling to fix hand fractures with Kirschner-wires (K-wires), a 3D printed hand phantom must not only be geometrically but also haptically correct. Due to a limited view during an operation, surgeons need to perfectly localize underlying risk structures only by feeling of specific bony protrusions of the human hand.}, subject = {Handchirurgie}, language = {en} } @article{MaierPerretSimonetal., author = {Maier, Johannes and Perret, Jerome and Simon, Martina and Schmitt-R{\"u}th, Stephanie and Wittenberg, Thomas and Palm, Christoph}, title = {Force-feedback assisted and virtual fixtures based K-wire drilling simulation}, series = {Computers in Biology and Medicine}, volume = {114}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, doi = {10.1016/j.compbiomed.2019.103473}, pages = {1 -- 10}, abstract = {One common method to fix fractures of the human hand after an accident is an osteosynthesis with Kirschner wires (K-wires) to stabilize the bone fragments. The insertion of K-wires is a delicate minimally invasive surgery, because surgeons operate almost without a sight. Since realistic training methods are time consuming, costly and insufficient, a virtual-reality (VR) based training system for the placement of K-wires was developed. As part of this, the current work deals with the real-time bone drilling simulation using a haptic force-feedback device. To simulate the drilling, we introduce a virtual fixture based force-feedback drilling approach. By decomposition of the drilling task into individual phases, each phase can be handled individually to perfectly control the drilling procedure. We report about the related finite state machine (FSM), describe the haptic feedback of each state and explain, how to avoid jerking of the haptic force-feedback during state transition. The usage of the virtual fixture approach results in a good haptic performance and a stable drilling behavior. This was confirmed by 26 expert surgeons, who evaluated the virtual drilling on the simulator and rated it as very realistic. To make the system even more convincing, we determined real drilling feed rates through experimental pig bone drilling and transferred them to our system. Due to a constant simulation thread we can guarantee a precise drilling motion. Virtual fixtures based force-feedback calculation is able to simulate force-feedback assisted bone drilling with high quality and, thus, will have a great potential in developing medical applications.}, subject = {Handchirurgie}, language = {en} } @inproceedings{MiddelPalmErdt, author = {Middel, Luise and Palm, Christoph and Erdt, Marius}, title = {Synthesis of Medical Images Using GANs}, series = {Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019}, booktitle = {Uncertainty for safe utilization of machine learning in medical imaging and clinical image-based procedures. First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019}, publisher = {Springer Nature}, address = {Cham}, isbn = {978-3-030-32688-3}, issn = {0302-9743}, doi = {10.1007/978-3-030-32689-0_13}, pages = {125 -- 134}, abstract = {The success of artificial intelligence in medicine is based on the need for large amounts of high quality training data. Sharing of medical image data, however, is often restricted by laws such as doctor-patient confidentiality. Although there are publicly available medical datasets, their quality and quantity are often low. Moreover, datasets are often imbalanced and only represent a fraction of the images generated in hospitals or clinics and can thus usually only be used as training data for specific problems. The introduction of generative adversarial networks (GANs) provides a mean to generate artificial images by training two convolutional networks. This paper proposes a method which uses GANs trained on medical images in order to generate a large number of artificial images that could be used to train other artificial intelligence algorithms. This work is a first step towards alleviating data privacy concerns and being able to publicly share data that still contains a substantial amount of the information in the original private data. The method has been evaluated on several public datasets and quantitative and qualitative tests showing promising results.}, subject = {Neuronale Netze}, language = {en} } @article{MaierDesernoHandelsetal., author = {Maier, Andreas and Deserno, Thomas M. and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, title = {Guest editorial of the IJCARS - BVM 2018 special issue}, series = {International Journal of Computer Assisted Radiology and Surgery}, volume = {14}, journal = {International Journal of Computer Assisted Radiology and Surgery}, publisher = {Springer}, doi = {10.1007/s11548-018-01902-0}, pages = {1 -- 2}, language = {en} } @article{BrownConsortiumZhouetal., author = {Brown, Peter and Consortium, RELISH and Zhou, Yaoqi and Palm, Christoph}, title = {Large expert-curated database for benchmarking document similarity detection in biomedical literature search}, series = {Database}, volume = {2019}, journal = {Database}, publisher = {Oxford University Pres}, doi = {10.1093/database/baz085}, pages = {1 -- 66}, abstract = {Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76\% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.}, subject = {Information Retrieval}, language = {en} } @article{EbigboMendelProbstetal., author = {Ebigbo, Alanna and Mendel, Robert and Probst, Andreas and Manzeneder, Johannes and De Souza Jr., Luis Antonio and Papa, Jo{\~a}o Paulo and Palm, Christoph and Messmann, Helmut}, title = {Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma}, series = {GuT}, volume = {68}, journal = {GuT}, number = {7}, publisher = {British Society of Gastroenterology}, doi = {10.1136/gutjnl-2018-317573}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-68}, pages = {1143 -- 1145}, abstract = {Computer-aided diagnosis using deep learning (CAD-DL) may be an instrument to improve endoscopic assessment of Barrett's oesophagus (BE) and early oesophageal adenocarcinoma (EAC). Based on still images from two databases, the diagnosis of EAC by CAD-DL reached sensitivities/specificities of 97\%/88\% (Augsburg data) and 92\%/100\% (Medical Image Computing and Computer-Assisted Intervention [MICCAI] data) for white light (WL) images and 94\%/80\% for narrow band images (NBI) (Augsburg data), respectively. Tumour margins delineated by experts into images were detected satisfactorily with a Dice coefficient (D) of 0.72. This could be a first step towards CAD-DL for BE assessment. If developed further, it could become a useful adjunctive tool for patient management.}, subject = {Speiser{\"o}hrenkrebs}, language = {en} } @article{PassosDeSouzaJrMendeletal., author = {Passos, Leandro A. and De Souza Jr., Luis Antonio and Mendel, Robert and Ebigbo, Alanna and Probst, Andreas and Messmann, Helmut and Palm, Christoph and Papa, Jo{\~a}o Paulo}, title = {Barrett's esophagus analysis using infinity Restricted Boltzmann Machines}, series = {Journal of Visual Communication and Image Representation}, volume = {59}, journal = {Journal of Visual Communication and Image Representation}, publisher = {Elsevier}, doi = {10.1016/j.jvcir.2019.01.043}, pages = {475 -- 485}, abstract = {The number of patients with Barret's esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett's esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @misc{EbigboMendelProbstetal., author = {Ebigbo, Alanna and Mendel, Robert and Probst, Andreas and Manzeneder, Johannes and De Souza Jr., Luis Antonio and Papa, Jo{\~a}o Paulo and Palm, Christoph and Messmann, Helmut}, title = {Artificial Intelligence in Early Barrett's Cancer: The Segmentation Task}, series = {Endoscopy}, volume = {51}, journal = {Endoscopy}, number = {04}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/s-0039-1681187}, pages = {6}, abstract = {Aims: The delineation of outer margins of early Barrett's cancer can be challenging even for experienced endoscopists. Artificial intelligence (AI) could assist endoscopists faced with this task. As of date, there is very limited experience in this domain. In this study, we demonstrate the measure of overlap (Dice coefficient = D) between highly experienced Barrett endoscopists and an AI system in the delineation of cancer margins (segmentation task). Methods: An AI system with a deep convolutional neural network (CNN) was trained and tested on high-definition endoscopic images of early Barrett's cancer (n = 33) and normal Barrett's mucosa (n = 41). The reference standard for the segmentation task were the manual delineations of tumor margins by three highly experienced Barrett endoscopists. Training of the AI system included patch generation, patch augmentation and adjustment of the CNN weights. Then, the segmentation results from patch classification and thresholding of the class probabilities. Segmentation results were evaluated using the Dice coefficient (D). Results: The Dice coefficient (D) which can range between 0 (no overlap) and 1 (complete overlap) was computed only for images correctly classified by the AI-system as cancerous. At a threshold of t = 0.5, a mean value of D = 0.72 was computed. Conclusions: AI with CNN performed reasonably well in the segmentation of the tumor region in Barrett's cancer, at least when compared with expert Barrett's endoscopists. AI holds a lot of promise as a tool for better visualization of tumor margins but may need further improvement and enhancement especially in real-time settings.}, subject = {Speiser{\"o}hrenkrankheit}, language = {en} } @article{EbigboPalmProbstetal., author = {Ebigbo, Alanna and Palm, Christoph and Probst, Andreas and Mendel, Robert and Manzeneder, Johannes and Prinz, Friederike and De Souza Jr., Luis Antonio and Papa, Jo{\~a}o Paulo and Siersema, Peter and Messmann, Helmut}, title = {A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology}, series = {Endoscopy International Open}, volume = {07}, journal = {Endoscopy International Open}, number = {12}, publisher = {Georg Thieme Verlag}, address = {Stuttgart}, doi = {10.1055/a-1010-5705}, pages = {1616 -- 1623}, abstract = {The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders. The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians. This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.}, subject = {Diagnose}, language = {en} }