Index
Similarity Learning for Writer Identification
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).
Object Consistency GAN for Object Detection Pretraining
Tomographic Projection Selection with Quantum Annealing
Introduction
The object of interest in computed tomography (CT) is exposed to X-rays from multiple angles. The radiation intensity measured by the detector opposite the radiation source then depends on the object’s density. Volumetric information about the object can be reconstructed using many such projection images. Another way to obtain projection data for reconstruction is single-photon emission computed tomography (SPECT), where a radioisotope is injected into the object, and the gamma rays emitted by radioactive decay are measured. The two methods have extensive applications in radiology but are restricted due to the harmful radiation emitted, which can damage cells in the human body [1].
There is a strong interest in performing the reconstruction task with a small number of projection images to limit the patient’s radiation exposure. Determining an optimal set of angles for projection data acquisition is referred to as projection selection. This set shall contain as few angles as possible while allowing a satisfactory reconstruction of the original object. Suppose some a priori information is available, e.g., in discrete tomography, where the object is known to consist of only a few materials with known densities. In that case, this can be used to improve projection selection algorithms. Some of these algorithms are compared in [2]. In particular, simulated annealing (SA) was proposed as a possible method for projection selection.
SA is a minimization method that allows a worsening of the current solution with some probability based on the slowly decreasing temperature of the system. The annealing process mimics the cooling of a material, which terminates in its lowest-energy state. Since a worsening of the current solution is accepted, the solution can not be “trapped” in a sub-optimal local minimum, as can happen with gradient-descent methods. A realistic annealing technique based on superconducting qubits is quantum annealing (QA). Quantum annealing is a quantum computing technique where quantum effects like superposition, entanglement, and tunneling can help traverse the barriers between local minima.
Methods
Starting from the SA formulation of the projection selection problem proposed in [2], a mathematical formulation as a quadratic unconstrained binary optimization (QUBO) problem will be given. The QUBO formulation can then be used to develop a program for the D-Wave quantum annealer, which will be run using simulation software. The discrete algebraic reconstruction technique (DART) will reconstruct the image from the selected projections. Using the images reconstructed by DART, the projection selection with QA can be compared to other projection selection algorithms.
Expected Results
Various iterative reconstruction methods are reviewed. In particular, a python implementation of the DART algorithm is provided, as it can perform an accurate reconstruction even from a small number of projections [3]. Furthermore, the projection selection problem in discrete tomography is formulated as a QUBO problem. This formulation will evaluate the possibility of running the projection selection problem using simulation software and a D-Wave quantum annealer.
[1] A. Maier, S. Steidl, V. Christlein, and J. Hornegger, “Medical imaging systems: An introductory guide,” 2018.
[2] L. Varga, P. Bal ́azs, and A. Nagy, “Projection selection algorithms for discrete tomography,” in Advanced Concepts
for Intelligent Vision Systems (J. Blanc-Talon, D. Bone, W. Philips, D. Popescu, and P. Scheunders, eds.), (Berlin,
Heidelberg), pp. 390–401, Springer Berlin Heidelberg, 2010.
[3] K. J. Batenburg and J. Sijbers, “Dart: A practical reconstruction algorithm for discrete tomography,” IEEE
Transactions on Image Processing, vol. 20, no. 9, pp. 2542–2553, 2011.
Machine learning based analysis of parts-of-speech in EEG data
Extraction of Treatment Margins from CT Scans for Evaluation of Lung Tumor Cryoablation
Thesis Description
Among all cancer types, lung cancer is responsible for the most deaths [1]. Cryoablation is a promising minimal
invasive method for treating lung cancer [2]. During percutaneous cryoablation, one or more probes are advanced
into the lung. Subsequently, a cycle of freezing and thawing using Argon gas achieves cell death [3]. Using
computed tomography (CT) images, the radiologist plans the type, number, and placement of probes based
on the expected geometry of the ice ball forming around each probe as provided by the manufacturer and the
tumor location.
The key parameter for assessing treatment success is to compare the margin created by the ablation around
the tumor with the desired safety margin. Margins of 2-10 mm [4] are required for eradication, depending
on tumor origin and type. The minimum safety margin required for eradication depends on the extent of
microscopic tumor extension beyond the tumor visible on CT.
Determining the margin is not a straight forward task, since it requires comparing CT scans taken before the
procedure to CT scans taken weeks or months later. Also, the ice ball forming during the procedure obscures
the tumor on subsequent CT scans. So far, radiologists evaluate treatment success in a binary yes/no manner
by mentally mapping 2D slices of pre- and post-procedure CT scans to mentally calculate treatment margins.
The goal of this thesis is to build an algorithm that evaluates treatment margins objectively and quantitatively,
leveraging readily available 3D CT imaging datasets. This algorithm may facilitate the early detection
of treatment failures in ex-post quality assurance and may ultimately also help estimate margins during the
procedure (i.e. to help decide for or against the addition of a probe).
From a technical point of view, the pre and post lung cryoablation 3D CT volumes have to be aligned
(registration task), tumors and ablation zones have to be either given, i.e., manually annotated, or automatically
generated (segmentation task) to compute and visualize geometrical margins.
Similar tools [5] [6] have been developed for microwave ablation which achieves cell death with high temperatures,
where tissue distortion of the tumor and surrounding tissue due to dehydration makes registration of pre
and post lung microwave ablation CT volumes difficult [7]. During cryoablation, dehydration does not occur
and tissue distortion is not noticeable. However, breathing is still expected to cause non-rigid deformation of
the volumes. Classical registration (i.e. SimpleElastix [8]) could be combined with unsupervised deep learning
approaches (i.e. Voxelmorph [9]) to achieve the desired registration.
To automatically segment tumors and ablation zones, a small convolutional neural network (CNN) could
be trained using the difference of the pre- and post-procedure scans as prior positional information. To assure
correct and time-efficient segmentation, a quality assurance step could be introduced in which a radiologist can
correct suggested segmentations.
To calculate the geometrical margin around the tumor volume, its parallel shifted surface is constructed
using an euclidean distance transform. The volumes of the tumor and the ablation zone should be visualized
by highlighting areas violating the targeted minimum margin and indicating proximity to blood vessels which
can act as thermal sinks [10].
To analyze the connections between clinical outcomes and pre/post CT imaging, applying end-to-end deep
learning would be the most desirable. However, since the amount of both labeled and unlabeled data is very
limited (approx. 50/300), machine learning methods could be applied to medically sensible features (e.g. margins)
derived from the tumor/ablation zone geometries. Alternatively a small CNN could be trained on these
geometries directly instead of the full scans.
Summary:
1. Register CT volumes
2. Segment tumors and ablation zones
3. Calculate and visualize margins and other features
4. Investigate relationships of features to outcomes of procedure
References
[1] Amanda McIntyre and Apar Kishor Ganti. Lung cancer a global perspective. Journal of Surgical Oncology,
115(5):550–554, 2017.
[2] Constantinos T. Sofocleous, Panagiotis Sideras, Elena N. Petre, and Stephen B. Solomon. Ablation for the
management of pulmonary malignancies. American Journal of Roentgenology, 197(4), 2011.
[3] Thierry de Baere, Lambros Tselikas, David Woodrum, et al. Evaluating cryoablation of metastatic lung tumors
in patientssafety and efficacy the eclipse trialinterim analysis at 1 year. Journal of Thoracic Oncology,
10(10):1468–1474, 2015.
[4] Impact of ablative margin on local tumor progression after radiofrequency ablation for lung metastases
from colorectal carcinoma: Supplementary analysis of a phase ii trial (mlcsg-0802). Journal of Vascular
and Interventional Radiology, 2022.
[5] Marco Solbiati, Riccardo Muglia, S. Nahum Goldberg, et al. A novel software platform for volumetric
assessment of ablation completeness. International Journal of Hyperthermia, 36(1):336–342, 2019. PMID:
30729818.
[6] Raluca-Maria Sandu, Iwan Paolucci, Simeon J. S. Ruiter, et al. Volumetric quantitative ablation margins
for assessment of ablation completeness in thermal ablation of liver tumors. Frontiers in Oncology, 11,
2021.
[7] Christopher L. Brace, Teresa A. Diaz, J. Louis Hinshaw, and Fred T. Lee. Tissue contraction caused by
radiofrequency and microwave ablation: A laboratory study in liver and lung. Journal of Vascular and
Interventional Radiology, pages 1280-1286, Aug 2010.
[8] Kasper Marstal, Floris Berendsen, Marius Staring, and Stefan Klein. Simpleelastix: A user-friendly, multilingual
library for medical image registration. In Proceedings of the IEEE conference on computer vision
and pattern recognition workshops, pages 134–142, 2016.
[9] Guha Balakrishnan, Amy Zhao, Mert R Sabuncu, John Guttag, and Adrian V Dalca. Voxelmorph: a
learning framework for deformable medical image registration. IEEE transactions on medical imaging,
38(8):1788–1800, 2019.
[10] P. David Sonntag, J. Louis Hinshaw, Meghan G. Lubner, Christopher L. Brace, and Fred T. Lee. Thermal
ablation of lung tumors. Surgical Oncology Clinics of North America, 20(2):369387, Aug 2011.