Master’s Thesis: Finding the Best Areas for Automated Blood Cell Analysis

Take your first step into working life where it truly matters—at CellaVision, a world-leading provider of digital solutions for

medical microscopy. We offer motivated students the opportunity to gain hands-on, meaningful experience through a Master’s thesis

project.

By working closely with our experienced specialists and leaders, you will gain valuable insights into both cutting-edge technology

and the daily operations of an innovative, global company. This collaboration gives you an early opportunity to build professional

relationships and explore a future career at CellaVision.

How can an automated microscope know which part of a blood smear is best suited for analysis? At CellaVision, we develop digital

microscopy solutions that support laboratories in analyzing blood cells. Choosing the right region of a slide is an important part

of producing reliable results.

Morphologists consider several characteristics when deciding which regions of a blood smear are suitable for analysis. These may

include how easily individual red blood cells can be distinguished, their distribution across the image, the amount of empty

background, and visible cell features. We want to investigate how these observations can be translated into an automated,

transparent and reliable method for assessing different areas of a blood smear.

In this thesis, you will explore how image characteristics such as overlapping red blood cells, central pallor, cell distribution,

and background regions can help identify suitable areas for automated analysis. You will first investigate the problem using

high-resolution images, where more detailed information is available, and then explore how to obtain a comparable assessment from

low-resolution images to support efficient scanning.

• Investigate how suitable analysis regions can be characterized and assessed, including a review of relevant literature and/or

discussions with experienced morphologists and other domain experts.

• Develop and evaluate an explainable image-analysis method, with machine learning as a central component, based on blood smear

images.

One or two students pursuing a Master's degree in Computer Science, Engineering Mathematics, Biomedical Engineering, or a related

field. You enjoy analytical problem-solving and programming and have experience with machine learning. An interest in image

processing and computer vision is also valuable.

You will work on a real medical imaging challenge together with experienced engineers at CellaVision, combining algorithm

development with practical evaluation. The goal is to produce a prototype and insights that may contribute to future improvements

in automated blood cell analysis.

The project will be carried out at our CellaVision HQ in Lund, Sweden—a modern office with great infrastructure and commuting

options.

If you are interested in partnering with CellaVision for your Master’s thesis during the spring of 2027, please send us your

application by November 30, 2026, at the latest.

CellaVision is an equal opportunity employer, and we encourage individuals from all backgrounds to apply. We celebrate diversity

and are committed to creating an inclusive environment for all employees, regardless of race, nationality, gender, age, sexual

orientation, disability, or any other characteristic.

CellaVision is a global leader in digital cell morphology within hematology, and our analyzers are trusted in healthcare services

around the world. We focus on innovation, working with the latest in programming, digital imaging, and machine learning to provide

value to our customers. By joining us, you'll be part of a creative, skilled team shaping the future of medical technology.