Creating AI Pipeline for Cell Image Analysis: Intel Edge AI SW Solutions (Part 2 of 2, Intel Edge AI in the Realm of Biopharma and Drug Development)

No items found.

Welcome back to our blog series on"Intel Edge AI in the Realm of Biopharma and Drug Development." Inthe first installment, we discussed the importance of Cell Analytics forAntibody Production in biopharmaceutical technology and drug development. Wehighlighted how AI pipelines are used to process brightfield images of cells,providing insights and addressing challenges in this field. Specifically, weexplored the CHO Cell Segmentation Use Case and noted that Intel has developeda reference implementation for deploying the CHO Cell Segmentation pipelineusing Intel edge AI software solutions.

Now, let's delve deeper into thespecifics of these Edge AI solutions: the OpenVINO toolkit, OpenVINO ModelServer, and AI Connect for Scientific Data. We'll explore how each of thesetools can play a crucial role in advancing biopharma and drug development.



optimizes, tunes, and runscomprehensive deep learning inferencing on general-purpose Intel architecture. Itis an open-source toolkit that accelerates AI inference with lower latency andhigher throughput while maintaining accuracy, reducing model footprint,and optimizing hardware use. It streamlines AI development and integration ofdeep learning in domains like computer vision, large language models (LLM), andgenerative AI.​

At the core of the OpenVINOtoolkit, we have the OpenVINO Runtime that loads and runs the models.The run-time employs plugins that are responsible for efficiently executing low-leveloperations that the deep learning model has on Intel HW. We have differentplug-ins for different HW, like CPU plugins, GPU plugins, and heterogeneousplugins.

The CPU plugin achieves a highperformance of neural networks on the CPU, using the Intel® Math Kernel Libraryfor Deep Neural Networks (Intel® MKL-DNN).

The GPU plugin uses the Intel® ComputeLibrary for Deep Neural Networks (clDNN) to infer deep neural networks on GPUs.

The heterogeneous plugin enablescomputing the inference of one network on several devices. The purposes ofexecuting networks in heterogeneous mode are to:

·        Utilize the power of accelerators to processthe heaviest parts of the network and to execute unsupported layers on fallbackdevices like the CPU.

·        Utilize all available hardware moreefficiently during one inference.


Another part of the OpenVINO toolkitis the model optimizer which optimizes and converts the model frompopular deep learning frameworks like TensorFlow, PyTorch, and ONNX, to OpenVINOintermediate representation format. The models are optimized withtechniques such as quantization, freezing, fusion, and more. Models can bedeployed across a mix of Intel® hardware and environments, on-premise andon-device, in the browser, or the cloud.

Besidesinference, OpenVINO provides the NeuralNetwork Compression Framework (NNCF) tool for implementing compressionalgorithms on models during training.

Figure1: OpenVINO™ overview. For detailed documentation about OpenVINO™ see:


OpenVINO™ Model Server (OVMS)

When it comes to deployment, youcan use OpenVINO Runtime, or you can use OpenVINO Model Server orOVMS for short.

OVMS is a scalable,high-performance tool for serving AI models and pipelines. It centralizes AI modelmanagement, ensuring consistent AI models across numerous devices, clouds,or compute nodes. Simply put, OVMS is a microservice that loads yourmodels, manages them, and exposes their capabilities through a network API,allowing other system components to interact with and utilize these models.OVMS supports two types of APIs—TensorFlow Serving and KServe compatible—whichprovide inference, model status, and model metadata services via gRPC orRESTful API2.

Why choose OVMS over OpenVINORuntime? There are several scenarios where OVMS is the better option. OpenVINOis a C++ project with an official Python binding, but what if your softwarestack is in another language? Implementing your own interface can be challenging.OVMS simplifies this by integrating OpenVINO into your system with pre-existingcapabilities. Additionally, if your system already operates in a microserviceparadigm, OVMS is an obvious choice. You might also prefer not to integrateOpenVINO directly into the business logic of other components or deal with thecomplexities of building the system. Moreover, if some applications run on lesspowerful devices, such as mobile phones, and you want to offload heavyinferencing to more powerful machines, OVMS can handle this by exposing anetwork API. Your components can run on multiple devices, sending data requeststo OVMS and receiving model outputs in response.

OVMS is ideal for scaling yoursolution. For instance, in a multi-node Kubernetes cluster, you can createmultiple replicas and set a load balancer in front of them, achieving highavailability and throughput beyond the capability of a single node. Thisaggregation is easily managed by OVMS.

For security and privacy, OVMSallows you to host your model server on a trusted machine, ensuring that otherapplications accessing it cannot see the model itself—only the exposed interface.

Besides these, for security andprivacy purposes OVMS enables you to host your model server on a machinethat you trust, and all the other applications that access it from inside oroutside cannot see your model. you just expose its interface with otherapplications and they can’t access or see the model.


Figure2. OpenVINO Model Server

Let’s examine the OVMS structure (Figure2). At the top, we have a network interface with gRPC and RESTful endpointssupporting TF Serving API and KServe API for inference and metadata calls.Metadata provides information on expected model inputs and outputs.

At next level, we haveconfiguration monitoring, scheduler, and model management. OVMS can servemultiple models simultaneously, specified in a configuration file, withbuilt-in model management and versioning. The model files don’t need to resideon a local file system; OVMS supports remote storage systems likeGoogle Cloud, AWS S3, and Azure. For learning more about OVMS please visit here.


AIConnect for Scientific Data (AiCSD)

AI Connect for Scientific Data (AiCSD)is an open-source software sample that connects data from scientificinstruments to AI pipelines and runs workloads at the edge.

It also manages pipelines for imageprocessing and automated image comparisons. AiCSD is a containerizedmicroservices-based solution utilizing open-source EdgeX Services and connectedby a secure Redis Message Broker and various communication APIs, whichmakes it adaptable for different use cases and settings. Figure 3 shows theservices created for this reference implementation.

The architectural components of AiCSDinclude:

·        Microservices: Provided by Intel, themicroservices include a user interface and applications for managing files andjobs.

·        EdgeX Application Services: AiCSDuses the APIs from the EdgeX Applications Services to communicate and transferinformation.

·        EdgeX Services:The services include the database, message broker, and security services.

·        Pipeline Execution: AiCSDfurnishes an example pipeline for pipeline management.

·        File System: AiCSD stores and manages inputand output files.

·        Third-party Input Devices: The devicessupply the images that will be processed. Examples include an opticalmicroscope or conveyor belt camera.


The reference architecture lets imagesbe processed using assigned jobs. The job tracks the movement of the file, thestatus, and any results or outputs from the pipeline. To process a job, sometasks help match information about a job to the appropriate pipeline to run.

The process can be elaborated asbelow:

1.       The InputDevice/Imager writes the file to the OEM file system in a directorythat is watched by the File Watcher. When the File Watcher detects thefile, it sends the job (JSON struct of particular fields) to the DataOrganizer via HTTP Request.

2.       The DataOrganizer sends the job to the Job Repository to create a new job inthe Redis Database. The job information is then sent to the TaskLauncher to determine if there is a task that matches the job. If there is,the job proceeds to the File Sender (OEM).

3.       The FileSender (OEM) is responsible for sending both the job and the file to the FileReceiver (Gateway). Once the File Receiver (Gateway) has written thefile to the Gateway file system, the job is then sent on to the TaskLauncher.

4.       The TaskLauncher verifies that there is a matching task for the job before sendingit to the appropriate pipeline using the EdgeX Message Bus (via Redis).The ML pipeline subscribes to the appropriate topic and processes the file inits pipeline. The output file (if there is one) is written to the file systemand the job is sent back to the Task Launcher.

5.       The TaskLauncher then decides if there is an output file or if there are just results.In the case of only results and no output file, the Task Launcher marks the jobas complete. If there is an output file, the Task Launcher sends the jobonward to the File Sender (Gateway).

6.       The FileSender (Gateway) publishes the job information to the EdgeXMessage Bus via Redis for the File Receiver (OEM) to subscribe andpull. The File Receiver (OEM) sends an HTTP request to the File Sender(Gateway) for the output file(s). The file(s) are sent as part of theresponse and the File Receiver (OEM) writes the output file(s) to thefile system.


Figure 3: Architecture andHigh-level Dataflow


AI Pipeline for CHOCell Segmentation Use Case

Let’s explain AI pipeline for CHOcell segmentation at a high level. As plate readers3 generate cellimages in their local file system, these images need to be transferred toanother device for analysis where AI software and hardware resources areavailable. This separation of data and model locations requires a flexible,microservice-based solution. We use the AiCSD microservice infrastructure totransfer the data to the edge compute device. AiCSD leverages EdgeX FoundryMicroservices to facilitate the automatic detection, management, and transferof scientific data. This microservice flexibility is crucial for addressing theheterogeneous system integration and asymmetric data interfacing inherent inthis project.

The AI pipeline on the edgecompute device includes image preprocessing, inference of multiple DeepLearning models optimized by the OpenVINO toolkit, and image postprocessing.Figure 4 shows an example of using Deep Learning models to process cell images,where UNet is used to mask and count MSC nuclei. These processes arecontainerized using BentoML,an open-source tool. Additionally, the OpenVINO Toolkit accelerates DeepLearning model inference, providing lower latency and higher throughput whilemaintaining accuracy and optimizing hardware usage. OVMS handles model managementand version control. Once the AI pipeline processing job is completed on theedge compute device, the final results are transferred back to the local filesystem of the original scientific device using the AiCSD microserviceinfrastructure to complete the task.

Overall, the integration of IntelEdge AI solutions enables the efficient implementation of the AI pipeline forthe CHO cell segmentation use case.


Figure 4. MSC Nuclei countingusing UNet deep learning model.



In the article, we discussed theimplementation of an AI pipeline for cell image analysis, particularly focusingon the application of Intel Edge AI solutions in processing brightfield cellimages. We highlighted Intel's OpenVINO toolkit as a crucial component foroptimizing the inference of existing Deep Learning models within the cell AIpipeline. Additionally, we explained how the OpenVINO Model Server operates asa microservice, enabling other components within a system to interact with andutilize the models effectively. Furthermore, we explored AI Connect forScientific Data (AiCSD) and its role in the efficient implementation of thebrightfield cell image analysis pipeline.

The journey toward fully realizing thecapabilities of AI in biopharma is ongoing, and Intel's contributions arepaving the way for a future where drug development is more agile, precise, andpatient-centric. Stay tuned for further insights as we continue to explore theexciting intersection of Edge AI technology and biopharmaceutical research.

Reach out to Intel's Health and LifeSciences team at or learn more about what we doat

We'd like to hear from you! Let usknow in the comments or discuss – which AI use cases in health and lifesciences do you think will have the greatest impact on global health?

If you enjoyed hearing from the Healthand Life Sciences team and want to hear more, give this post a like andensure you subscribe to get the latest updates from theteam. 


About the Author

Nooshin Nabizadeh has Electrical and Computer Engineering from the University of Miami and worksat Intel Corporation as AI Solutions Architect. She enjoys photography, writingpoetry, reading about psychology and philosophy, and optimizing solutions torun as fast as possible on a given piece of hardware. Connect with her onLinkedIn by mentioning thisblog.



1.      Brightfieldmicroscopy is a widely used technique for observing the morphology of cells andtissues.


3.      A plate reader is a laboratoryinstrument used to obtain images from samples in microtiter plates. The readershines a specific calibrated frequency of light (UV, visible, fluorescence,etc.) through the samples in the wells of the plate. Plate reader microscopydata sets have inherent variability which drives the requirement of regulartracked calibration and adjustment.