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Instrument-integrated optical sensors are gaining popularity in microsurgery due to their ability to provide accurate measurements of instrument-to-tissue distances, enabling precise instrument control. However, obstructions in the optical path can result in measurement errors. In this work, we propose a method to improve robustness of distance information from sensorized microsurgical instruments. Our pipeline integrates a rapid search algorithm to identify relevant neighboring data points, as well as geometric and non-geometric techniques to accurately model the local tissue structure. Additionally, we implement a fusion of measurement and model to identify and overcome disturbances, e.g., obstructions from surgical instruments or semantic segmentation errors. Our simulation examines the effect of different modeling parameters and techniques on distance prediction, yielding a mean absolute error of less than 0.02 mm when using the local spline fit. Experiments in ex vivo human eyes show that our pipeline achieves up to 89

Accurate plant phenotyping is essential for crop breeding and the development of health monitoring systems. Traditional phenotyping methods, such as visual observation and leaf counting, are inefficient and labor-intensive. Although automated approaches are more efficient, they still require extensive labeled datasets for training segmentation networks, which can be costly and time-consuming to prepare. This paper builds on Williams et al. and uses SAM for initial segmentation, then compares two approaches for selecting leaf segments: geometric and color-based filtering with automatically determined thresholds, and a lightweight CNN trained on minimal data. The CNN method delivers superior performance, with an Average Recall AR75 of 63 % and an Average Precision AP75 of 58 % (IoU threshold = 75 %) using only four training images, and minimal annotation work. These findings highlight the potential of combining SAM with CNN-based filtering for robust plant phenotyping applications, offering a scalable solution that makes advanced phenotyping more accessible and less dependent on extensive data preparation. Ph & auml;notypisierung ist ein zentrales Werkzeug f & uuml;r die Z & uuml;chtung und & Uuml;berwachung von Nutzpflanzen, doch herk & ouml;mmliche Verfahren wie visuelle Beobachtung und manuelle Blattz & auml;hlung sind arbeitsintensiv und wenig skalierbar. Moderne, automatisierte Ans & auml;tze beruhen meist auf & uuml;berwachten Segmentierungsverfahren und erfordern umfangreich annotierte Datens & auml;tze. In dieser Arbeit bauen wir auf den Ergebnissen von Williams et al. auf und nutzen das Segment Anything Model (SAM) f & uuml;r eine initiale, weitgehend datenunabh & auml;ngige Segmentierung. Darauf aufbauend vergleichen wir zwei Strategien zur Auswahl relevanter Blattsegmente: (i) geometrische und farbbasierte Filterung mit automatisch bestimmten Schwellenwerten und (ii) ein leichtgewichtiges CNN, das mit einem Minimaldatensatz trainiert wird. Die CNN-basierte Methode erreicht eine durchschnittliche Recall (AR75) von 63 % und eine durchschnittliche Precision (AP75) von 58 % bei Verwendung von lediglich vier Trainingsbildern. Dies unterstreicht das Potenzial der Kombination von Vision-Foundation-Modellen wie SAM mit CNN-gest & uuml;tzter Filterung f & uuml;r robustes, skalierbares Pflanzen-Ph & auml;notypisierung und reduziert zugleich die Abh & auml;ngigkeit von umfangreicher Datenannotation.

Inference over large-scale foundation models within heterogeneous edge environments necessitates a fundamentally reconfigurable orchestration substrate. Static partitioning of model layers presumes temporal stability across compute and network resources, which is misaligned with the volatility of real-world deployments. We introduce a framework in which both the spatial placement and internal segmentation of foundation models are elevated to runtime-resolved constructs. The orchestration problem is formalized as a constrained optimization over layer-wise assignments, subject to evolving latency, utilization, and privacy gradients. The framework implements reactive inference composition responsive to infrastructural fluctuations by integrating model-aware capacity profiling with dynamic graph re-partitioning and reallocation. We introduce architectural and algorithmic components, along with a representative use case in 6G multi-access edge computing.

The fragility of ocular tissues combined with the limited surgical workspace demands precise instrument control and focus, making sensor-integrated robotic systems a promising solution. In this paper, we introduce a surgical system for telemanipulated endolaser photocoagulation that leverages instrument-integrated optical coherence tomography (iiOCT) for accurate distance measurement. We have developed a controller that maintains a specified instrument-to-retina distance, complemented by haptic shared control to assist the ophthalmic surgeon throughout the procedure. We conducted a pilot study involving 12 participants, including an expert vitreoretinal surgeon, to evaluate the system's performance across three levels of user assistance. The distance-based controller demonstrated a significant improvement in axial precison compared to telemanipulated trials, achieving a mean error of 4 micrometers and a standard deviation of 69 micrometers across all subjects. Preliminary experiments conducted on porcine eyes confirmed the feasibility of our approach on ex vivo tissues.

Realistic surgical simulation plays a crucial role in training novice surgeons and in the development of autonomous agents. World models can scale such simulation environments to realistic and diverse procedures by predicting future patient states conditioned on current observations and surgical actions. However, current state-of-the-art approaches often fail to satisfy key criteria required for clinical applicability, including visual realism, physically grounded interactions, and the ability to simulate scenarios beyond the training distribution. Hence, we introduce SWoMo, a neuro-symbolic world model for cataract surgery simulation that decouples motion generation from visual realism. The symbolic component, consisting of a rule-based simulator and scene graph representations, models motion dynamics and tool-tissue interactions, while a diffusion model produces realistic visual appearance, including textures and tissue deformations. We propose an inverse pairing strategy that reconstructs real surgical videos in the simulator to obtain paired simulated and real videos, which are then used to train our video diffusion model for the reverse objective of sim-to-real translation. Our experiments show both qualitative and quantitative improvements over prior work. We demonstrate that our simulator further satisfies the key criteria, including generalisation to unseen interaction geometries, improvements in downstream phase detection, and unsupervised video style transfer. The code, data, and model weights are available at: https://ssharvienkumar.github.io/SWoMo/