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CA125 represents the circulating subunit of the large transmembrane glycoprotein MUC 16. Elevated levels of CA125 are suggestive of ovarian cancer and the magnitude of elevation correlates with burden of disease. A rising CA125 level after treatment suggests cancer recurrence. Ovarian tumor cells produce and shed MUC 16 which can bind to certain peripheral immune cells. To study this, current techniques use labelled-fluorescent markers that bind to CA125 on cells and flow cytometry to detect mean fluorescence. A limitation is that this technique may not be able to distinguish low-binding events from autofluorescence. Our new technology improves the resolution for studying CA125 bound to immune cells by employing antibody-labelled gold nanoparticles with far-greater sensitivity than fluorescent markers. The cells are then examined using darkfied microscopy rather than flow, which also permits automated slide scanning and focusing for 3-dimensional counting. This enables both simultaneous use of fluorescent markers to identify the immune cell populations as well as automated image analysis to quantify the amount of bound CA125 on individual cells. Preliminary data show that MUC 16 is preferentially bound to NK and T cells in women with ovarian cancer compared to binding in healthy controls. These insights may help elucidate the function of CA125 in ovarian cancer and improve our understanding of immune-tumor interactions. Citation Format: Kornel Lakatos, Petra Krauledat, German Gonzalez Serrano, Peter Hansen, Daniel William Cramer, Kevin Elias, Manish Patankar, Jawad Hoballah. A research tool for elucidating the interaction between human mucin 16 (CA125) and immune cells in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3178.

Many automatic image analysis algorithms in medical imaging require a good initialization to work properly. A similar problem occurs in many imaging-based clinical workflows, which depend on anatomical landmarks. The localization of anatomic structures based on a defined context provides with a solution to that problem, which turns out to be more challenging in medical imaging where labeled images are difficult to obtain. We propose a two-stage process to detect and regress 2D bounding boxes of predefined anatomical structures based on a 2D surrounding context. First, we use a deep convolutional neural network (DCNN) architecture to detect the optimal slice where an anatomical structure is present, based on relevant landmark features. After this detection, we employ a similar architecture to perform a 2D regression with the aim of proposing a bounding box where the structure is encompassed. We trained and tested our system for 57 anatomical structures defined in axial, sagittal and coronal planes with a dataset of 504 labeled Computed Tomography (CT) scans. We compared our method with a well-known object detection algorithm (Viola Jones) and with the inter-rater error for two human experts. Despite the relatively small number of scans and the exhaustive number of structures analyzed, our method obtained promising and consistent results, which proves our architecture very generalizable to other anatomical structures.

There is currently a division between real-world human performance and the decision making of socially interactive robots. This circumstance is partially due to the difficulty in estimating human cues, such as pose and gesture, from robot sensing. Towards bridging this division, we present a method for kinematic pose estimation and action recognition from monocular robot vision through the use of dynamical human motion vocabularies. Our notion of a motion vocabulary is comprised of movement primitives that structure a human's action space for decision making and predict human movement dynamics. Through prediction, such primitives can be used to both generate motor commands for specific actions and perceive humans performing those actions. In this paper, we focus specifically on the perception of human pose and performed actions using a known vocabulary of primitives. Given image observations over time, each primitive infers pose independently using its expected dynamics in the context of a particle filter. Pose estimates from a set of primitives inferencing in parallel are arbitrated to estimate the action being performed. The efficacy of our approach is demonstrated through interactive-time pose and action recognition over extended motion trials. Results evidence our approach requires small numbers of particles for tracking, is robust to unsegmented multi-action movement, movement speed, camera viewpoint and is able to recover from occlusions.

We have presented a neuro-inspired method for monocular tracking and action recognition for movement imitation. Our approach combines vocabularies of kinematic motion learned offline with online estimation of a demonstrator's underlying virtual trajectory. A modular approach to pose estimation is taken for computational tractability and emulation of structures hypothesized in neuroscience. Our current results suggest our method can perform tracking and recognition from partial observations at interactive rates. Our current system demonstrates robustness with respect to the viewpoint of the camera, the speed of performance of the action, and recovery from ambiguous situations.