In this paper, we propose a workflow algorithm for timely tracking of the tool tip during cell manipulation using a template-based approach augmented with low level feature detection. Doing so addresses the problem of adverse influences on template-based tracking during tool-cell interaction while maintaining an efficient track-servo framework. This consideration is important in developing autonomous robotic vision-guided micromanipulators. Our method facilitates vision-guided micromanipulation autonomously without manual interventions even during tool-cell interaction. This is done by decomposing the process to four scenarios that operate on their respective mode. The self-initializing mode is first used to localize and focus a region of interest (ROI) which the tip lies in. Once in focus, the tip is manipulated using a unified visual track-servo template-based approach. A reinitialization mechanism will be triggered to prevent tracking from being interrupted by partial cell occlusion of the tracking ROI. This mechanism uses the self-initializing concept combining motion cue and low-level feature detection to localize the needle tip. Following the reinitialization, we further recover tracking of the needle tip using a mechanism that updates the base template. This adaptive approach ensures uninterrupted tracking even when the cell is interacting with the tool and under deformation. Results demonstrated that with the newly incorporated mechanisms, the localized position improved from an error of more than 50% to less than 10% of the specimen size. When there is no specimen in the scene the new workflow shows no adverse effect on the localization through 270 tracked frames. By incorporating reinitialization and recovery to this workflow algorithm, we hope to initiate the first step towards uncalibrated autonomous vision-guided micromanipulation process. I. INTRODUCTION Recent advancements in micromanipulation improve the usability, functionality, speed, accuracy, and repeatability in cell manipulation. The usability and functionality are enhanced through design-centric manipulation mechanisms [1, 2], intelligent image-guidance [3, 4] and intuitive operation interfaces [5, 6]. In addition, robotic vision-guided micromanipulators facilitate shorter operation time, higher precision, and better consistency [7].

In this paper, an automatic vision-guided micromanipulation approach to facilitate versatile deployment and portable setup is proposed. This work is motivated by the importance of micromanipulation and the limitations in existing automation technology in micromanipulation. Despite significant advancements in micromanipulation techniques, there remain bottlenecks in integrating and adopting automation for this application. An underlying reason for the gaps is the difficulty in deploying and setting up such systems. To address this, we identified two important design requirements, namely portability and versatility of the micromanipulation platform. A selfcontained vision-guided approach requiring no complicated preparation or setup is proposed. This is achieved through an uncalibrated self-initializing workflow algorithm also capable of assisted targeting. The feasibility of the solution is demonstrated on a low-cost portable microscope camera and compact actuated micro-stages. Results suggest subpixel accuracy in localizing the tool tip during initialization steps. The self-focus mechanism could recover intentional blurring of the tip by autonomously manipulating it 95.3 % closer to the focal plane. The average error in visual servo is less than a pixel with our depth compensation mechanism showing better maintaining of similarity score in tracking. Cell detection rate in a 1637-frame video stream is 97.7% with subpixels localization uncertainty. Our work addresses the gaps in existing automation technology in the application of robotic vision-guided micromanipulation and potentially contribute to the way cell manipulation is performed. Note to Practitioners-This paper introduces an automatic method for micromanipulation using visual information from microscopy. We design an automatic workflow, which consists of 1) self-initialization, 2) vision-guided manipulation and 3) assisted targeting, and demonstrate versatile deployment of the micromanipulator on a portable microscope camera setup. Unlike existing systems, our proposed method does not require any tedious calibration or expensive setup making it mobile and lowcost. This overcomes the constraints of traditional practices that confine automated cell manipulation to a laboratory setting. By extending the application beyond the laboratory environment, automated micromanipulation technology can be made more ubiquitous and expands readily to facilitate field study.

. Significance: Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool that can measure resting-state functional connectivity; however, non-neuronal components present in fNIRS signals introduce false discoveries in connectivity, which can impact interpretation of functional networks. Aim: We investigated the effect of short channel correction on resting-state connectivity by removing non-neuronal signals from fNIRS long channel data. We hypothesized that false discoveries in connectivity can be reduced, hence improving the discriminability of functional networks of known, different connectivity strengths. Approach: A principal component analysis-based short channel correction technique was applied to resting-state data of 10 healthy adult subjects. Connectivity was analyzed using magnitude-squared coherence of channel pairs in connectivity groups of homologous and control brain regions, which are known to differ in connectivity. Results: By removing non-neuronal components using short channel correction, significant reduction of coherence was observed for oxy-hemoglobin concentration changes in frequency bands associated with resting-state connectivity that overlap with the Mayer wave frequencies. The results showed that short channel correction reduced spurious correlations in connectivity measures and improved the discriminability between homologous and control groups. Conclusions: Resting-state functional connectivity analysis with short channel correction performs better than without correction in its ability to distinguish functional networks with distinct connectivity characteristics.

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