Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network

The objective investigation of the dynamic properties of vocal fold vibrations demands the recording and further quantitative analysis of laryngeal high-speed video (HSV). Quantification of the vocal fold vibration patterns requires as a first step the segmentation of the glottal area within each video frame from which the vibrating edges of the vocal folds are usually derived. Consequently, the outcome of any further vibration analysis depends on the quality of this initial segmentation process. In this work we propose for the first time a procedure to fully automatically segment not only the time-varying glottal area but also the vocal fold tissue directly from laryngeal high-speed video (HSV) using a deep Convolutional Neural Network (CNN) approach. Eighteen different Convolutional Neural Network (CNN) network configurations were trained and evaluated on totally 13,000 high-speed video (HSV) frames obtained from 56 healthy and 74 pathologic subjects. The segmentation quality of the best performing Convolutional Neural Network (CNN) model, which uses Long Short-Term Memory (LSTM) cells to take also the temporal context into account, was intensely investigated on 15 test video sequences comprising 100 consecutive images each. As performance measures the Dice Coefficient (DC) as well as the precisions of four anatomical landmark positions were used. Over all test data a mean Dice Coefficient (DC) of 0.85 was obtained for the glottis and 0.91 and 0.90 for the right and left vocal fold (VF) respectively. The grand average precision of the identified landmarks amounts 2.2 pixels and is in the same range as comparable manual expert segmentations which can be regarded as Gold Standard. The method proposed here requires no user interaction and overcomes the limitations of current semiautomatic or computational expensive approaches. Thus, it allows also for the analysis of long high-speed video (HSV)-sequences and holds the promise to facilitate the objective analysis of vocal fold vibrations in clinical routine. The here used dataset including the ground truth will be provided freely for all scientific groups to allow a quantitative benchmarking of segmentation approaches in future

Deep Learning-Based Particle Detection and Instance Segmentation for Microscopy Images

Bildgebende mikroskopische Verfahren ermöglichen Forschern, Einblicke in komplexe, bisher unverstandene Prozesse zu gewinnen. Um den Forschern den Weg zu neuen Erkenntnissen zu erleichtern, sind hoch-automatisierte, vielseitige, genaue, benutzerfreundliche und zuverlässige Methoden zur Partikeldetektion und Instanzsegmentierung erforderlich. Diese Methoden sollten insbesondere für unterschiedliche Bildgebungsbedingungen und Anwendungen geeignet sein, ohne dass Expertenwissen für Anpassungen erforderlich ist. Daher werden in dieser Arbeit eine neue auf Deep Learning basierende Methode zur Partikeldetektion und zwei auf Deep Learning basierende Methoden zur Instanzsegmentierung vorgestellt. Der Partikeldetektionsansatz verwendet einen von der Partikelgröße abhängigen Hochskalierungs-Schritt und ein U-Net Netzwerk für die semantische Segmentierung von Partikelmarkern. Nach der Validierung der Hochskalierung mit synthetisch erzeugten Daten wird die Partikeldetektionssoftware BeadNet vorgestellt. Die Ergebnisse auf einem Datensatz mit fluoreszierenden Latex-Kügelchen zeigen, dass BeadNet Partikel genauer als traditionelle Methoden detektieren kann. Die beiden neuen Instanzsegmentierungsmethoden verwenden ein U-Net Netzwerk mit zwei Decodern und werden für vier Objektarten und drei Mikroskopie-Bildgebungsverfahren evaluiert. Für die Evaluierung werden ein einzelner nicht balancierter Trainingsdatensatz und ein einzelner Satz von Postprocessing-Parametern verwendet. Danach wird die bessere Methode in der Cell Tracking Challenge weiter validiert, wobei mehrere Top-3-Platzierungen und für sechs Datensätze eine mit einem menschlichen Experten vergleichbare Leistung erreicht werden. Außerdem wird die neue Instanzsegmentierungssoftware microbeSEG vorgestellt. microbeSEG verwendet, analog zu BeadNet, OMERO für die Datenverwaltung und bietet Funktionen für die Erstellung von Trainingsdaten, das Trainieren von Modellen, die Modellevaluation und die Modellanwendung. Die qualitativen Anwendungen von BeadNet und microbeSEG zeigen, dass beide Tools eine genaue Auswertung vieler verschiedener Mikroskopie-Bilddaten ermöglichen. Abschließend gibt diese Dissertation einen Ausblick auf den Bedarf an weiteren Richtlinien für Bildanalyse-Wettbewerbe und Methodenvergleiche für eine zielgerichtete zukünftige Methodenentwicklung

Automatic Bone Structure Segmentation of Under-Sampled CT/FLT-PET Volumes for HSCT Patients

In this thesis I present a pipeline for the instance segmentation of vertebral bodies from joint CT/FLT-PET image volumes that have been purposefully under-sampled along the axial direction to limit radiation exposure to vulnerable HSCT patients. The under-sampled image data makes the segmentation of individual vertebral bodies a challenging task, as the boundaries between the vertebrae in the thoracic and cervical spine regions are not well resolved in the CT modality, escaping detection by both humans and algorithms. I train a multi-view, multi-class U-Net to perform semantic segmentation of the vertebral body, sternum, and pelvis object classes. These bone structures contain marrow cavities that, when viewed in the FLT-PET modality, allow us to investigate hematopoietic cellular proliferation in HSCT patients non-invasively. The proposed convnet model achieves a Dice score of 0.9245 for the vertebral body object class and shows qualitatively similar performance on the pelvis and sternum object classes. The final instance segmentation is realized by combining the initial vertebral body semantic segmentation with the associated FLT-PET image data, where the vertebral boundaries become well-resolved by the 28th day post-transplant. The vertebral boundary detection algorithm is a hand-crafted spatial filter that enforces vertebra span as an anatomical prior, and it performs similar to a human for the detection of all but one vertebral boundary in the entirety of the HSCT patient dataset. In addition to the segmentation model, I propose, design, and test a “drop-in” replacement up-sampling module that allows state-of-the-art super-resolution convnets to be used for purely asymmetric upscaling tasks (tasks where only one image dimension is scaled while the other is held to unity). While the asymmetric SR convnet I develop falls short of the initial goal, where it was to be used to enhance the unresolved vertebral boundaries of the under-sampled CT image data, it does objectively upscale medical image data more accurately than naïve interpolation methods and may be useful as a pre-processing step for other medical imaging tasks involving anisotropic pixels or voxels