TY - JOUR A1 - Osterholt, Tobias A1 - Salber, Dagmar A1 - Matusch, Andreas A1 - Becker, Johanna Sabine A1 - Palm, Christoph T1 - IMAGENA: Image Generation and Analysis BT - An Interactive Software Tool handling LA-ICP-MS Data JF - International Journal of Mass Spectrometry N2 - Metals are involved in many processes of life. They are needed for enzymatic reactions, are involved in healthy processes but also yield diseases if the metal homeostasis is disordered. Therefore, the interest to assess the spatial distribution of metals is rising in biomedical science. Imaging metal (and non-metal) isotopes by laser ablation mass spectrometry with inductively coupled plasma (LA-ICP-MS) requires a special software solution to process raw data obtained by scanning a sample line-by-line. As no software ready to use was available we developed an interactive software tool for Image Generation and Analysis (IMAGENA). Unless optimised for LA-ICP-MS, IMAGENA can handle other raw data as well. The general purpose was to reconstruct images from a continuous list of raw data points, to visualise these images, and to convert them into a commonly readable image file format that can be further analysed by standard image analysis software. The generation of the image starts with loading a text file that holds a data column of every measured isotope. Specifying general spatial domain settings like the data offset and the image dimensions is done by the user getting a direct feedback by means of a preview image. IMAGENA provides tools for calibration and to correct for a signal drift in the y-direction. Images are visualised in greyscale as well a pseudo-colours with possibilities for contrast enhancement. Image analysis is performed in terms of smoothed line plots in row and column direction. KW - LA-ICP-MS KW - ICP-Massenspektrometrie KW - Bilderzeugung KW - Graphische Benutzeroberfläche KW - Image generation KW - Image analysis KW - Graphical user interface Y1 - 2011 U6 - https://doi.org/10.1016/j.ijms.2011.03.010 VL - 307 IS - 1-3 SP - 232 EP - 239 ER - TY - JOUR A1 - Dammers, Jürgen A1 - Axer, Markus A1 - Gräßel, David A1 - Palm, Christoph A1 - Zilles, Karl A1 - Amunts, Katrin A1 - Pietrzyk, Uwe T1 - Signal enhancement in polarized light imaging by means of independent component analysis JF - NeuroImage N2 - Polarized light imaging (PLI) enables the evaluation of fiber orientations in histological sections of human postmortem brains, with ultra-high spatial resolution. PLI is based on the birefringent properties of the myelin sheath of nerve fibers. As a result, the polarization state of light propagating through a rotating polarimeter is changed in such a way that the detected signal at each measurement unit of a charged-coupled device (CCD) camera describes a sinusoidal signal. Vectors of the fiber orientation defined by inclination and direction angles can then directly be derived from the optical signals employing PLI analysis. However, noise, light scatter and filter inhomogeneities interfere with the original sinusoidal PLI signals. We here introduce a novel method using independent component analysis (ICA) to decompose the PLI images into statistically independent component maps. After decomposition, gray and white matter structures can clearly be distinguished from noise and other artifacts. The signal enhancement after artifact rejection is quantitatively evaluated in 134 histological whole brain sections. Thus, the primary sinusoidal signals from polarized light imaging can be effectively restored after noise and artifact rejection utilizing ICA. Our method therefore contributes to the analysis of nerve fiber orientation in the human brain within a micrometer scale. KW - Bildgebendes Verfahren KW - Polarisiertes Licht KW - Signalverarbeitung KW - Signaltrennung KW - Komponentenanalyse KW - Gehirn Y1 - 2010 U6 - https://doi.org/10.1016/j.neuroimage.2009.08.059 VL - 49 IS - 2 SP - 1241 EP - 1248 PB - Elsevier ER - TY - JOUR A1 - Palm, Christoph A1 - Axer, Markus A1 - Gräßel, David A1 - Dammers, Jürgen A1 - Lindemeyer, Johannes A1 - Zilles, Karl A1 - Pietrzyk, Uwe A1 - Amunts, Katrin T1 - Towards ultra-high resolution fibre tract mapping of the human brain BT - registration of polarised light images and reorientation of fibre vectors JF - Frontiers in Human Neuroscience N2 - Polarised light imaging (PLI) utilises the birefringence of the myelin sheaths in order to visualise the orientation of nerve fibres in microtome sections of adult human post-mortem brains at ultra-high spatial resolution. The preparation of post-mortem brains for PLI involves fixation, freezing and cutting into 100-μm-thick sections. Hence, geometrical distortions of histological sections are inevitable and have to be removed for 3D reconstruction and subsequent fibre tracking. We here present a processing pipeline for 3D reconstruction of these sections using PLI derived multimodal images of post-mortem brains. Blockface images of the brains were obtained during cutting; they serve as reference data for alignment and elimination of distortion artefacts. In addition to the spatial image transformation, fibre orientation vectors were reoriented using the transformation fields, which consider both affine and subsequent non-linear registration. The application of this registration and reorientation approach results in a smooth fibre vector field, which reflects brain morphology. PLI combined with 3D reconstruction and fibre tracking is a powerful tool for human brain mapping. It can also serve as an independent method for evaluating in vivo fibre tractography. KW - Bildgebendes Verfahren KW - Dreidimensionale Bildverarbeitung KW - Polarisiertes Licht KW - Gehirnkarte Y1 - 2010 U6 - https://doi.org/10.3389/neuro.09.009.2010 VL - 4 ER - TY - JOUR A1 - Axer, Markus A1 - Amunts, Katrin A1 - Gräßel, David A1 - Palm, Christoph A1 - Dammers, Jürgen A1 - Axer, Hubertus A1 - Pietrzyk, Uwe A1 - Zilles, Karl T1 - Novel Approach to the Human Connectome BT - Ultra-High Resolution Mapping of Fiber Tracts in the Brain JF - NeuroImage N2 - Signal transmission between different brain regions requires connecting fiber tracts, the structural basis of the human connectome. In contrast to animal brains, where a multitude of tract tracing methods can be used, magnetic resonance (MR)-based diffusion imaging is presently the only promising approach to study fiber tracts between specific human brain regions. However, this procedure has various inherent restrictions caused by its relatively low spatial resolution. Here, we introduce 3D-polarized light imaging (3D-PLI) to map the three-dimensional course of fiber tracts in the human brain with a resolution at a submillimeter scale based on a voxel size of 100 μm isotropic or less. 3D-PLI demonstrates nerve fibers by utilizing their intrinsic birefringence of myelin sheaths surrounding axons. This optical method enables the demonstration of 3D fiber orientations in serial microtome sections of entire human brains. Examples for the feasibility of this novel approach are given here. 3D-PLI enables the study of brain regions of intense fiber crossing in unprecedented detail, and provides an independent evaluation of fiber tracts derived from diffusion imaging data. KW - Connectome KW - Human brain KW - Method KW - Polarized light imaging KW - Tractography KW - Systems biology KW - Bildgebendes Verfahren KW - Dreidimensionale Bildverarbeitung KW - Polarisiertes Licht KW - Gehirnkarte Y1 - 2011 U6 - https://doi.org/10.1016/j.neuroimage.2010.08.075 VL - 54 IS - 2 SP - 1091 EP - 1101 ER - TY - GEN A1 - Gräßel, David A1 - Axer, Markus A1 - Palm, Christoph A1 - Dammers, Jürgen A1 - Amunts, Katrin A1 - Pietrzyk, Uwe A1 - Zilles, Karl T1 - Visualization of Fiber Tracts in the Postmortem Human Brain by Means of Polarized Light T2 - NeuroImage KW - Gehirn KW - Bildgebendes Verfahren KW - Polarisiertes Licht KW - Pathologische Anatomie Y1 - 2009 U6 - https://doi.org/10.1016/S1053-8119(09)71415-6 VL - 47 IS - Suppl. 1 SP - 142 ER - TY - JOUR A1 - Souza Jr., Luis Antonio de A1 - Mendel, Robert A1 - Strasser, Sophia A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Messmann, Helmut A1 - Papa, João Paulo A1 - Palm, Christoph T1 - Convolutional Neural Networks for the evaluation of cancer in Barrett’s esophagus: Explainable AI to lighten up the black-box JF - Computers in Biology and Medicine N2 - Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their level of accountability and transparency must be provided in such evaluations. The reliability related to machine learning predictions must be explained and interpreted, especially if diagnosis support is addressed. For this task, the black-box nature of deep learning techniques must be lightened up to transfer its promising results into clinical practice. Hence, we aim to investigate the use of explainable artificial intelligence techniques to quantitatively highlight discriminative regions during the classification of earlycancerous tissues in Barrett’s esophagus-diagnosed patients. Four Convolutional Neural Network models (AlexNet, SqueezeNet, ResNet50, and VGG16) were analyzed using five different interpretation techniques (saliency, guided backpropagation, integrated gradients, input × gradients, and DeepLIFT) to compare their agreement with experts’ previous annotations of cancerous tissue. We could show that saliency attributes match best with the manual experts’ delineations. Moreover, there is moderate to high correlation between the sensitivity of a model and the human-and-computer agreement. The results also lightened that the higher the model’s sensitivity, the stronger the correlation of human and computational segmentation agreement. We observed a relevant relation between computational learning and experts’ insights, demonstrating how human knowledge may influence the correct computational learning. KW - Deep Learning KW - Künstliche Intelligenz KW - Computerunterstützte Medizin KW - Barrett's esophagus KW - Adenocarcinoma KW - Machine learning KW - Explainable artificial intelligence KW - Computer-aided diagnosis Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-20126 SN - 0010-4825 VL - 135 SP - 1 EP - 14 PB - Elsevier ER - TY - JOUR A1 - Brown, Peter A1 - Consortium, RELISH A1 - Zhou, Yaoqi A1 - Palm, Christoph T1 - Large expert-curated database for benchmarking document similarity detection in biomedical literature search JF - Database N2 - 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. KW - Information Retrieval KW - Indexierung KW - Literaturdatenbank KW - Dokument KW - Ähnlichkeitssuche KW - Suchmaschine Y1 - 2019 U6 - https://doi.org/10.1093/database/baz085 VL - 2019 SP - 1 EP - 66 PB - Oxford University Pres ER - TY - GEN A1 - Ebigbo, Alanna A1 - Mendel, Robert A1 - Probst, Andreas A1 - Manzeneder, Johannes A1 - Souza Jr., Luis Antonio de A1 - Papa, João Paulo A1 - Palm, Christoph A1 - Messmann, Helmut T1 - Artificial Intelligence in Early Barrett's Cancer: The Segmentation Task T2 - Endoscopy N2 - 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. KW - Speiseröhrenkrankheit KW - Maschinelles Lernen KW - Barrett's esphagus KW - Deep Learning KW - Segmentation Y1 - 2019 U6 - https://doi.org/10.1055/s-0039-1681187 VL - 51 IS - 04 SP - 6 PB - Georg Thieme Verlag CY - Stuttgart ER - TY - CHAP A1 - Chang, Ching-Sheng A1 - Lin, Jin-Fa A1 - Lee, Ming-Ching A1 - Palm, Christoph ED - Tolxdorff, Thomas ED - Deserno, Thomas M. ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph T1 - Semantic Lung Segmentation Using Convolutional Neural Networks T2 - Bildverarbeitung für die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin N2 - Chest X-Ray (CXR) images as part of a non-invasive diagnosis method are commonly used in today’s medical workflow. In traditional methods, physicians usually use their experience to interpret CXR images, however, there is a large interobserver variance. Computer vision may be used as a standard for assisted diagnosis. In this study, we applied an encoder-decoder neural network architecture for automatic lung region detection. We compared a three-class approach (left lung, right lung, background) and a two-class approach (lung, background). The differentiation of left and right lungs as direct result of a semantic segmentation on basis of neural nets rather than post-processing a lung-background segmentation is done here for the first time. Our evaluation was done on the NIH Chest X-ray dataset, from which 1736 images were extracted and manually annotated. We achieved 94:9% mIoU and 92% mIoU as segmentation quality measures for the two-class-model and the three-class-model, respectively. This result is very promising for the segmentation of lung regions having the simultaneous classification of left and right lung in mind. KW - Neuronales Netz KW - Segmentierung KW - Brustkorb KW - Deep Learning KW - Encoder-Decoder Network KW - Chest X-Ray Y1 - 2020 SN - 978-3-658-29266-9 U6 - https://doi.org/10.1007/978-3-658-29267-6_17 SP - 75 EP - 80 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Middel, Luise A1 - Palm, Christoph A1 - Erdt, Marius T1 - Synthesis of Medical Images Using GANs T2 - 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 N2 - 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. KW - Neuronale Netze KW - Deep Learning KW - Generative adversarial networks KW - Machine Learning KW - Artificial Intelligence KW - Data privacy KW - Deep Learning KW - Bilderzeugung KW - Datenschutz Y1 - 2019 SN - 978-3-030-32688-3 U6 - https://doi.org/10.1007/978-3-030-32689-0_13 SN - 0302-9743 SP - 125 EP - 134 PB - Springer Nature CY - Cham ER -