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

About the role

Start your career with 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.

The Assignment

Master's Thesis Project: Finding the Best Areas for Automated Blood Cell Analysis. 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.

Responsibilities

• 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.
• Identify and evaluate image characteristics that distinguish suitable and less suitable blood smear regions.
• Develop and evaluate an explainable image‑analysis method, with machine learning as a central component, based on blood smear images.
• Explore how insights from high‑resolution images can be translated into efficient assessments using low‑resolution images.
• Evaluate the results against existing approaches and, where possible, assessments by experienced morphologists.

Requirements

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.

What You Can Expect

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. Thesis level: Master’s thesis (30 ECTS). The project will be carried out at our CellaVision HQ in Lund, Sweden—a modern office with great infrastructure and commuting options.

Application deadline: November 30, 2026.