nnUNet-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery Planning.

Segmentation of the thoracic region and breast tissues is crucial for analyzing and diagnosing the presence of breast masses. This paper introduces a medical image segmentation architecture that aggregates two neural networks based on the state-of-the-art nnU-Net. Additionally, this study proposes a polyvinyl alcohol cryogel (PVA-C) breast phantom, based on its automated segmentation approach, to enable planning and navigation experiments for robotic breast surgery. The dataset consists of multimodality breast MRI of T2W and STIR images obtained from 10 patients. A statistical analysis of segmentation tasks emphasizes the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. We first use a single class labeling to segment the breast region and then exploit it as an input for three-class labeling to segment fatty, fibroglandular (FGT), and tumorous tissues. The first network has a 0.95 DCS, while the second network has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively. Clinical Relevance-This research is relevant to the breast surgery community as it establishes a deep learning-based (DL) algorithmic and phantomic foundation for surgical planning and navigation that will exploit preoperative multimodal MRI and intraoperative ultrasound to achieve highly cosmetic breast surgery. In addition, the planning and navigation will guide a robot that can cut, resect, bag, and grasp a tissue mass that encapsulates breast tumors and positive tissue margins. This image-guided robotic approach promises to potentiate the accuracy of breast surgeons and improve patient outcomes.

HANDLING PERCEPTION UNCERTAINTY IN SIMULATION BASED SINGULATION PLANNING FOR ROBOTIC BIN PICKING.

Robotic bin picking requires using a perception system to estimate the pose of the parts in the bin. The selected singulation plan should be robust with respect to perception uncertainties. If the estimated posture is significantly different from the actual posture, then the singulation plan may fail during execution. In such cases, the singulation process will need to be repeated. We are interested in selecting singulation plans that minimize the expected task completion time. In order to estimate the expected task completion time for a proposed singulation plan, we need to estimate the probability of success and the plan execution time. Robotic bin picking needs to be done in real-time. Therefore, candidate singulation plans need to be generated and evaluated in real-time. This paper presents an approach for utilizing computationally efficient simulations for generating singulation plans. Results from physical experiments match well with predictions obtained from simulations.

Self discovery enables robot social cognition: are you my teacher?

Infants exploit the perception that others are 'like me' to bootstrap social cognition (Meltzoff, 2007a). This paper demonstrates how the above theory can be instantiated in a social robot that uses itself as a model to recognize structural similarities with other robots; this thereby enables the student to distinguish between appropriate and inappropriate teachers. This is accomplished by the student robot first performing self-discovery, a phase in which it uses actuation-perception relationships to infer its own structure. Second, the student models a candidate teacher using a vision-based active learning approach to create an approximate physical simulation of the teacher. Third, the student determines that the teacher is structurally similar (but not necessarily visually similar) to itself if it can find a neural controller that allows its self model (created in the first phase) to reproduce the perceived motion of the teacher model (created in the second phase). Fourth, the student uses the neural controller (created in the third phase) to move, resulting in imitation of the teacher. Results with a physical student robot and two physical robot teachers demonstrate the effectiveness of this approach. The generalizability of the proposed model allows it to be used over variations in the demonstrator: The student robot would still be able to imitate teachers of different sizes and at different distances from itself, as well as different positions in its field of view, because change in the interrelations of the teacher's body parts are used for imitation, rather than absolute geometric properties.