Automated lung cancer lesions segmentation in 18F-FDG PET/CT

18F-FDG PET/CT is routinely employed as valuable clinical tool for non-invasive staging of lung cancer patients. In particular, the presence and location of tumor-harboring lesions is a key determinant of lung cancer stage, prognosis, and optimal treatment. Recently, deep learning algorithms have shown promising results for automated identification of sites suspicious for tumor in 18F-FDG PET/CT for different cancer types and have potential
to support physicians in accurate image assessment. Nevertheless, a limited per-lesion accuracy for primary tumors and lymph nodes in patients with lung cancer has been reported. The aim of this thesis is to develop a deep learning algorithm for improved automated
detection and delineation of lung cancer lesions in 18F-FDG PET/CT.
In particular, the Master’s thesis covers the following aspects:
1. Exploration of state-of-the-art deep learning architectures for automatic segmentation of lesions in lung cancer PET/CT medical images.
2. Implementation of a deep learning architecture and training using different parameters to find high-accuracy segmentation results.
3. Evaluation of the impact of different PET image quality characteristics on the performance of the deep learning algorithm by varying parameters of the PET reconstruction algorithm and simulating lower count rates.
4. Applying changes to the architecture or modify the loss to make the deep learning algorithm more robust to variations in PET image quality.
5. Comparing the performance and accuracy with other methods available in the literature.
6. Generation of artificial data with similar anatomic location and classification as originally annotated by a specialized physician (optional).