TU Eindhoven – PhD student for esophageal cancer detection

“PhD student to investigate automated image analysis for esophageal cancer detection in volumetric laser endomicroscopy scans”

Job description
Volumetric Laser Endomicroscopy (VLE) is a recent endoscopic imaging technique that allows gastroenterologists to inspect the inner tissue layers of the esophageal wall. This provides endoscopists with a very useful tool for the detection of early esophageal cancer, which typically develops in these subsurface layers that cannot be imaged by traditional endoscopic imaging techniques. During a VLE image capturing procedure, a balloon is endoscopically inserted in the esophagus. Next, the balloon is inflated to stretch the tissue into a fixed position after which a laser scans a cylindrical segment (in a horizontal plane) of up to 6 cm in height in 90 seconds, resulting in 1,200 circumferential scans of 4,096 × 1,024 pixels.

The fast and efficient procedure VLE offers, is particularly beneficial for patients with a so called Barrett’s Esophagus (BE). In patients with BE, due to consistent gastric reflux, a segment of the esophagus proximal to the gastro-esophageal junction is covered with an acid resistant tissue-type that is not native to the esophagus. This process results in an over 30 times higher chance of developing esophageal adenocarcinoma, which accounts for the majority of esophageal cancers in the Western World.

Early cancer in BE has proven to be very hard to recognize using traditional white-light endoscopy. Hence, over the past years, studies have presented automated analysis algorithms for endoscopic imagery in order to aid the physician in detecting early esophageal cancer. Although these methods might increase the detection ratio of early Barrett’s cancer, they suffer from a major drawback: what is not imaged by the endoscopists, is not processed by the detection algorithm. Consequently, if a BE segment is overlooked, developing cancers might go unnoticed. Therefore, VLE offers a promising alternative, as it can image the entire Barrett segment in a single scan. On top of that, it allows the analysis of the subsurface tissue layers, which is impossible with traditional white-light endoscopy.

Although the above-mentioned aspects of VLE are very attractive, there are two major difficulties that this novel technique faces: (1) physicians struggle to interpret the complex VLE signal which is quite noisy and unstructured, and (2) carefully inspecting 1,200 high-resolution VLE slices is very laborious and time consuming. Therefore, we aim to develop a Computer-Aided Detection (CADe) algorithm that automatically inspects a full VLE volume for signs of early cancer.

Project consortium
The open PhD position is part of a project funded by the Dutch Cancer Society and technology foundation STW and it involves a collaborative effort of Eindhoven University of Technology (TU/e), the Catharina Hospital in Eindhoven, the Academic Medical Center in Amsterdam and Ninepoint Medical from Boston, which is the manufacturer of the VLE imaging device. Next to the technical expertise offered by the TU/e and Ninepoint Medical, a network of leading medical experts on early Barrett’s cancer with a strong track record on innovative imaging methods are involved in this project. For the requested PhD researcher, it is envisioned that the candidate will work in close cooperation with a medical researcher at the AMC and the experts in both involved hospitals.


We are looking for talented, motivated, and enthusiastic candidates with an MSc degree in Electrical Engineering, Computer Science, or Biomedical Engineering with a background in machine learning, signal processing and/or computer vision. The candidate should match the following profile.

  • Experience in using Matlab, Python and/or TensorFlow;
  • Excellent knowledge of image (signal) processing and machine learning;
  • Team player that enjoys to work in multicultural teams with interest in oncology;
  • Strong communication skills, including excellent proficiency in English (spoken and written);
  • Preferably: knowledge on validation methods and statistical testing.

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