Master Thesis: Deep Learning for Bone Tumor Detection and Segmentation: 2D vs 3D

Jun 12, 2024

Abstract:

The detection and segmentation of bone tumors using magnetic resonance imaging (MRI) have crucial implications for clinical diagnosis and treatment planning. With the advent of deep learning techniques, there’s a growing interest in leveraging these methods to analyze MRI bone tumor images. However, a fundamental question arises: Is 3D volumetric processing superior to traditional 2D slice-by-slice processing in deep learning tasks for MRI bone tumor analysis? This research addresses this question by evaluating the effectiveness of 2D versus 3D deep learning methodologies.

Methodology:

The methodology involves implementing several deep-learning models to compare the efficacy of 2D and 3D techniques for MRI bone tumor analysis:

Literature review on the current state-of-the-art techniques in 2D and 3D MRI-based detection and segmentation tasks using deep learning.
Implement tumor detection and segmentation models in both 2D and 3D.
Explore and implement a hybrid approach that combines the strengths of both 2D and 3D processing methods to achieve superior results.
Presenting and discussing results.

Prerequisites:

Advanced knowledge of deep learning with imaging data;
Beneficial but not necessary: experience in medicine/oncology;
Preferred starting date: September 2024 (with flexibility);

What we offer:

Very rare medical data with high potential for publication.
Highly educated & interdisciplinary environment.
Top-level hardware for scientific computing.
Constant feedback from medical and computer science experts

How to apply:

Send an email to anna.curto-vilalta@tum.de, with your CV and small introduction about you and your motivation.

References:

He, Avesta, et al. “Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.” Bioengineering 10 (2023): 181.

Ushinsky, A., et al. “A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.” American Journal of Roentgenology 216, no. 1 (2021): 111-116.

Wang, H., et al. “Mixed 2D and 3D Convolutional Network with Multi-Scale Context for Lesion Segmentation in Breast DCE-MRI.” Biomedical Signal Processing and Control 68 (2021): Article no. 102607.