A topological sampling theorem for Robust boundary reconstruction and image segmentation

AbstractExisting theories on shape digitization impose strong constraints on admissible shapes, and require error-free data. Consequently, these theories are not applicable to most real-world situations. In this paper, we propose a new approach that overcomes many of these limitations. It assumes that segmentation algorithms represent the detected boundary by a set of points whose deviation from the true contours is bounded. Given these error bounds, we reconstruct boundary connectivity by means of Delaunay triangulation and α-shapes. We prove that this procedure is guaranteed to result in topologically correct image segmentations under certain realistic conditions. Experiments on real and synthetic images demonstrate the good performance of the new method and confirm the predictions of our theory

Improved Locally Adaptive Sampling Criterion for Topology Preserving Reconstruction of Multiple Regions

Volume based digitization processes often deal with non-manifold shapes. Even though many reconstruction algorithms have been proposed for non-manifold surfaces, they usually don’t preserve topological properties. Only recently, methods were presented which—given a finite set of surface sample points—result in a mesh representation of the original boundary preserving all or certain neighbourhood relations, even if the sampling is sparse and highly noise corrupted. We show that the required sampling conditions of the algorithm called “refinement reduction” limit the guaranteed correctness of the outcome to a small class of shapes. We define new locally adaptive sampling conditions that depend on our new pruned medial axis and finally prove without any restriction on shapes that under these new conditions, the result of “refinement reduction” corresponds to a superset of a topologically equivalent mesh

Isar/Athen: Griechische Künstler in München – Deutsche Künstler in Griechenland [Ergebnisse der Tagung "Isar/Athen. Griechische Künstler in München - Deutsche Künstler in Griechenland" ... im Zentralinstitut für Kunstgeschichte München am 13. April 2007]

Die 1808 gegründete Münchner Kunstakademie war über lange Zeit ein 'Magnetfeld' von internationaler Dimension und zog zahlreiche Studenten aus dem Ausland, insbesondere aus den USA, aber auch aus dem gesamten mittel-, ost- und südosteuropäischen Raum an. Einige dieser ausländischen Künstler blieben nach Abschluss ihres Studiums in München, eröffneten hier eigene Kunstschulen oder wurden selbst zu Lehrern an der Akademie ernannt. Sie trugen ebenso wie die in ihre Heimatländer zurückgekehrten Künstler wesentlich zum Ruf Münchens als einer 'Kunststadt' bei. Dieser Band enthält die Beiträge der Tagung "Isar/Athen. Griechische Künstler in München - Deutsche Künstler in Griechenland" (siehe http://www.zikg.eu/main/2007/isar-athen/index.htm). Die Beziehungen zwischen München und Griechenland waren aufgrund der politischen Gegebenheiten von besonders enger Natur. Die Texte schlagen einen Bogen vom 19. Jahrhundert über Giorgio de Chirico bis zu gemeinsamen Projekten der Münchner und Athener Akademien der letzten Jahrzehnte

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

Purpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multicenter setting including more difficult recognition tasks such as surgical action and surgical skill. Methods: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. Results: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n = 9 teams), for instrument presence detection between 38.5% and 63.8% (n = 8 teams), but for action recognition only between 21.8% and 23.3% (n = 5 teams). The average absolute error for skill assessment was 0.78 (n = 1 team). Conclusion: Surgical workflow and skill analysis are promising technologies to support the surgical team, but there is still room for improvement, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost importance to create more open, high-quality datasets in order to allow the development of artificial intelligence and cognitive robotics in surgery