Abstract
Standardization and efficiency enhancement in veterinary laboratory diagnostics rely on the integration and automation of specimen preparation, observation, imaging, and analysis workflows. Traditional microscopes often encounter issues such as unstable imaging quality, complex interface compatibility, and operational workflow interruptions during digitalization. To address this challenge, this study explores a technical solution for deeply integrating a camera module with standard interfaces and stable imaging performance into a new fully automated veterinary microscope system. This integration aims to seamlessly embed high-definition digital imaging capabilities into automated slide preparation and observation workflows, thereby establishing a continuous, reliable workflow from sample preparation to digital image output. This approach meets the dual demands of efficiency and consistency required by modern veterinary pathology.
I. Imaging Bottlenecks and Integration Requirements for Automated Veterinary Microscopes
The design objective of modern fully automated veterinary microscopes is to complete the entire process—from slide preparation and staining of blood or tissue samples to digital imaging—within a limited timeframe. This goal imposes high demands on system integration and module coordination. Among these, the digital imaging module serves as the critical link between physical samples and subsequent computer analysis. Its performance directly determines the quality of images obtained by pathologists, thereby impacting diagnostic accuracy. Currently, many systems employ imaging solutions with limitations in compatibility, image stability, or operational convenience. Examples include the need for additional driver installations, restricted image parameter adjustment ranges, or inconsistent color reproduction under varying lighting conditions. These factors can become efficiency bottlenecks or sources of error within automated workflows. Therefore, adopting a standardized imaging module that is plug-and-play, delivers stable image quality, and allows for software-based adjustments is considered an effective approach to optimize overall system reliability and user experience.
II. Technical Characteristics of the Imaging Module and Its Adaptability Value in Microscopy Systems
The imaging module examined in this study offers targeted solutions to the aforementioned challenges through its design parameters and functional characteristics. The module employs a 1/5-inch image sensor with a single pixel size of 1.6μm. This larger pixel size enhances signal-to-noise ratio and dynamic range under the faint light conditions often encountered in microscopic optical systems. It ensures sufficient image detail and gradation capture when observing lightly stained or highly transparent specimens.
Optically, the module's lens offers an 80-degree field of view (FOV) with a focal length range from 25mm to 40mm. This capability allows adaptation to the image field coverage requirements of different magnification objectives on microscopes. Particularly when observing large-area smears under low-power objectives, it reduces the number of image stitching operations, thereby improving scanning efficiency. The lens aperture is set at F2.8±5%, ensuring adequate light transmission while providing moderate depth-of-field control. This capability helps mitigate minor surface irregularities during automated scanning.
The module outputs MJPEG-formatted 1920x1080 full HD video streams at 20-30fps. This not only meets the demand for high-definition capture of static samples but also provides smooth real-time preview for potential dynamic observations (e.g., locating specific fields of view). Its integrated Auto Exposure Control (AEC), Auto White Balance (AWB), and Auto Gain Control (AGC) algorithms adaptively handle color and brightness variations caused by different staining methods (e.g., Giemsa staining, Diff-Quick staining), reducing manual intervention and ensuring consistency in batch sample imaging.
Crucially, the module fully complies with the UVC (USB Video Class) protocol, enabling driverless plug-and-play functionality. This feature allows seamless integration into the microscope's embedded control system without complex driver installation or debugging, significantly reducing system integration complexity and long-term maintenance costs. Additionally, the module supports enhanced connectivity flexibility through USB 2.0 OTG protocol. Its open image parameter adjustment interface allows system software to preset and recall specific combinations of brightness, contrast, saturation, and gamma values for different sample types (e.g., blood smears, cell centrifuge smears, tissue imprints). It even enables fine-tuning of color balance to account for the distinct staining characteristics of eosinophils and basophils, thereby optimizing visual recognition of specific cells.
Regarding mechanical and electrical reliability, the module employs a 6-pin solder interface, operates at 5V DC, and has a typical power consumption of 100-120mA, meeting embedded device requirements for low power consumption and stable power supply. Its structure is purposefully engineered with threaded adhesives and sealants applied at specified lengths and positions to reinforce critical mechanical joints. Bending and installation stresses on the FPC are standardized to withstand minor vibrations and stress variations that may occur during automated stage movements or mechanical adjustments after integration within the microscope.
III. Systematic Enhancement of Veterinary Microscope System Performance Through Module Integration
Integrating this imaging module into fully automated veterinary microscopes delivers systemic workflow enhancement rather than merely supplementary functionality. The microscope's existing automated slide transport, focusing, and scanning mechanisms precisely position target areas at the field of view center. The integrated high-definition imaging module serves as its “digital eye,” converting optical images into high-quality, reproducible digital signals in real time.
This integration achieves true continuity in the “preparation-imaging” process. For instance, after completing autofocus for a field of view, the system can immediately trigger the module to capture high-definition frames. The image data, along with positional information, is then transmitted to computer software for stitching or analysis—without requiring the operator to switch devices or adjust imaging settings. The module's plug-and-play functionality and consistent color performance ensure comparability between images acquired across different instruments and time points—critical for long-term monitoring or multi-person collaborative diagnosis.
From an ergonomic and operational simplification perspective, complex imaging parameter configurations are encapsulated within the microscope control software. Pre-set modes such as “Hematology” and “Cytology” enable users to select settings with a single click, significantly lowering the technical barrier for operating the digital imaging component. The module's robust mechanical design also ensures compatibility with veterinary laboratory environments, working in tandem with the microscope's overall housing to protect internal precision optical and mechanical components.
IV. Conclusion: Advancing Toward Integrated and Intelligent Veterinary Laboratory Diagnostics
By deeply integrating standardized, high-performance imaging modules into fully automated veterinary microscopy systems, this research demonstrates a clear technical pathway to enhance the reliability, efficiency, and consistency of veterinary laboratory digital workflows. This integration not only addresses common pain points in the digital imaging segment of current automated systems but also establishes a stable, adjustable digital image source that lays the groundwork for more advanced applications. These include AI-based preliminary cell morphology screening, automated pathogen identification, or quantitative analysis.
This solution demonstrates that by adopting modular imaging components validated for medical environments, medical device developers can focus more intently on their core competencies—automated mechanical systems and software algorithm development—thereby accelerating product iteration and ultimately driving veterinary pathology diagnosis toward greater integration, intelligence, and standardization.
