Automated detection of live cells and microspheres in low contrast bright field microscopy

Bollavaram, Manasa; Sane, Parth; Chowdhury, Sagar; Gupta, Satyandra K.; Banerjee, Ashis G.

doi:10.1109/marss.2016.7561703

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Random Sample Consensus (RANSAC) was used in combination with data filtering and optimal convergence process in order to detect and track micro-grippers and micro-objects in micro-assembly tasks [19]. Finally, Bollavaram et al presented an accurate and robust method to detect the 2D positions and orientations of micro-scale objects in low contrast bright field microscopy [20]. The main reason for estimating the 2D pixel positions of miniaturized agents is related to significant technical difficulties in tracking the agents in 3D.…”

Section: Introductionmentioning

confidence: 99%

A GPU-accelerated model-based tracker for untethered submillimeter grippers

Scheggi

Yoon

Ghosh

et al. 2018

Robotics and Autonomous Systems

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Miniaturized grippers that possess an untethered structure are suitable for a wide range of tasks, ranging from micromanipulation and microassembly to minimally invasive surgical interventions. In order to robustly perform such tasks, it is critical to properly estimate their overall configuration. Previous studies on tracking and control of miniaturized agents estimated mainly their 2D pixel position, mostly using cameras and optical images as a feedback modality. This paper presents a novel solution to the problem of estimating and tracking the 3D position, orientation and configuration of the tips of submillimeter grippers from marker-less visual observations. We consider this as an optimization problem, which is solved using a variant of the Particle Swarm Optimization algorithm. The proposed approach has been implemented in a Graphics Processing Unit (GPU) which allows a user to track the submillimeter agents online. The proposed approach has been evaluated on several image sequences obtained from a camera and on B-mode ultrasound images obtained from an ultrasound probe. The sequences show the grippers moving, rotating, opening/closing and grasping biological material. Qualitative results obtained using both hydrogel (soft) and metallic (hard) grippers with different shapes and sizes ranging from 750 microns to 4 mm (tip to tip), demonstrate the capability of the proposed method to track the agent in all the video sequences. Quantitative results obtained by processing synthetic data reveal a tracking position error of 25 ± 7 μm and orientation error of 1.7 ± 1.3 degrees. We believe that the proposed technique can be applied to different stimuli responsive miniaturized agents, allowing the user to estimate the full configuration of complex agents from visual marker-less observations.

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Section: Introductionmentioning

confidence: 99%

A GPU-accelerated model-based tracker for untethered submillimeter grippers

Scheggi

Yoon

Ghosh

et al. 2018

Robotics and Autonomous Systems

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“…Moreover, some of the methods are not suitable for real-time implementation [37] or only work for specific cell shapes [38]. We have developed a method to address many of these limitations and perceive objects of varying types and shapes from a series of time-lapse images taken at the same cross-sectional plane [39]. Our method is based on a novel combination of many well-known image processing techniques.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Perception Method for Low Contrast Bright Field Microscopy in Heterogeneous Microenvironments

et al. 2017

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Automated optical tweezers-based robotic manipulation of microscale objects requires real-time visual perception for estimating the states, i.e., positions and orientations, of the objects. Such visual perception is particularly challenging in heterogeneous environments comprising mixtures of biological and colloidal objects, such as cells and microspheres, when the popular imaging modality of low contrast bright field microscopy is used. In this paper, we present an accurate method to address this challenge. Our method combines many well-established image processing techniques such as blob detection, histogram equalization, erosion, and dilation with a convolutional neural network in a novel manner. We demonstrate the effectiveness of our processing pipeline in perceiving objects of both regular and irregular shapes in heterogeneous microenvironments of varying compositions. The neural network, in particular, helps in distinguishing the individual microspheres present in dense clusters.

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