[Master's Thesis] - AI-guided Three-dimensional Transfer Function for Automatic Path Tracing of CT Data

22.01.2026, Diplomarbeiten, Bachelor- und Masterarbeiten

In volumetric visualization, transfer functions are typically manually parameterized to map CT scan densities to specific colors, allowing for the differentiation of anatomical structures in the rendered images. This project aims to automate this mapping and incorporate 3D segmentation information, enabling automatic colorization and the ability to distinguish between tissues with similar densities.

The Human-Centered Computing and Extended Reality Lab of the Professorship for Machine Intelligence in Orthopedics seeks applicants for Master Thesis

Project Description

CT scans are routinely used in medical procedures, with grayscale slices serving as the standard representation. However, 3D visualizations, particularly those generated via path tracing, offer more realistic and intuitive depictions of anatomical structures. Since CT data contains only density values, an appropriate mapping from density to color is required. Fine-tuning these transfer functions for specific use cases is often time-consuming and challenging to automate. This project investigates how deep learning-based segmentation can be leveraged to drive the automatic generation of transfer functions, thereby reducing manual effort while improving adaptability. Furthermore, we aim to incorporate the positional information from the segmentation into the transfer function, enabling more fine-grained control. For this, we will run automatic segmentation, automatic transfer function generation, and extend an existing path tracer to load the segmentation information in addition to the raw transfer function.

Key Research Areas

• Applying deep learning for CT scan segmentation
• Exploring methods to approximate densities from segmented regions and designing user interfaces (visual or parametric) for adjustments
• Investigating techniques for automated transfer function creation and optimization
• Path tracer scripting for visualization integration

Technical Prerequisites (Or motivation to learn)

• Knowledge of computer graphics
• Interest in visualization and path tracing
• Experience with deep learning model application
• Interest in working with medical data
• Experience with C++ and Python

Please send your transcript of records, CV and motivation to: Constantin Kleinbeck (constantin.kleinbeck@tum.de) with CC to hex-thesis.ortho@mh.tum.de

Literature
[1] D. Engel, L. Sick, and T. Ropinski, “Leveraging Self-Supervised Vision Transformers for Segmentation-based Transfer Function Design,” IEEE Trans. Visual. Comput. Graphics, pp. 1–12, 2024, doi: 10.1109/TVCG.2024.3401755. [2] P. Ljung, J. Krüger, E. Groller, M. Hadwiger, C. D. Hansen, and A. Ynnerman, “State of the Art in Transfer Functions for Direct Volume Rendering,” Computer Graphics Forum, vol. 35, no. 3, pp. 669–691, 2016, doi: 10.1111/cgf.12934. [3] J. Wasserthal et al., “TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images,” Radiology: Artificial Intelligence, vol. 5, no. 5, p. e230024, Sep. 2023, doi: 10.1148/ryai.230024. [4] M. Berger, J. Li, and J. A. Levine, “A Generative Model for Volume Rendering,” IEEE Trans. Visual. Comput. Graphics, vol. 25, no. 4, pp. 1636–1650, Apr. 2019, doi: 10.1109/TVCG.2018.2816059. [5] J. Winslow, Y. Zhang, and E. Samei, “A method for characterizing and matching CT image quality across CT scanners from different manufacturers,” Med. Phys., vol. 44, no. 11, pp. 5705–5717, Nov. 2017, doi: 10.1002/mp.12554.

Kontakt: constantin.kleinbeck@tum.de