Index

Analysis of Recorded Single-Channel Patch-Clamp Timeseries Using Neural Networks Trained with Simulated Data

On how to learn and use the Detectability Index efficiently for CT trajectory optimisation

Optimizing the CT scan trajectory is crucial for industrial computed tomography as it can enhance the quality
of image reconstruction and reduce scanning time. However, determining the optimal trajectory is challenging
due to the large solution space of the nondeterministic polynomial time-hard optimization problem.
The objective of this study is to propose a suitable architecture for optimizing the trajectory of a robot-based
computed tomography (CT) system. This architecture aims to improve the quality of reconstructed images,
effectively representing the detectability index for a given task.
The goal of this optimization is to reduce artefacts in the CT images and potentially decrease the scanning time.
To achieve this objective, the proposed method requires a CAD model of the test specimen, simulates possible
X-ray projections and predicts the detectability index using a suitable regression neural network architecture.

Statistical Evaluation of Orca Vocalization Activity

Evaluation of imperfect segmentation labels and the influence on deep learning models

Multi-organ segmentation in CT is of great clinical and research value [1], which can benefit the development of automatic computer-aided diagnosis tools and the accuracy of some interventional therapies, such as the treatment planning of radiation therapy. With the development of the deep learning (DL), the performance of the DL-based models has dramatically improved, compared with traditional segmentation methods [2].
For training a DL model for segmentation task, a paired segmentation dataset is needed. A paired segmentation dataset here indicates the accurate annotation of all voxels in all CT volumes, which is tedious and time-consuming. For this reason, the large-scale segmentation datasets for multiple organs in large body region are rarely published and mostly contain annotation errors. Several researches have been done to study the influence of imperfect segmentation labels on the training of the segmentation network, but to the best of our knowledge, none is done for the multi-organ segmentation task in CT. [3, 4]
In this thesis, our research problem is how the segmentation network will be influenced by the typical annotation errors. To achieve this, several typical annotation errors will be simulated on a public multiorgan segmentation dataset, CT-ORG, [5] and the influence will be analysed both quantitatively and qualitatively.

The thesis will comprise the following work items:

Literature overview of related analysis of imperfect segmentation labels.
Simulate some typical annotation errors on a segmentation dataset.
Train the baseline model on the perfect dataset and the models on the imperfect datasets.
Evaluate the influence of the errors with the baseline.
Record the result in the thesis

[1] Andreas Maier, Christopher Syben, Tobias Lasser, and Christian Riess. A gentle introduction to deep learning in medical image processing. Zeitschrift f¨ur Medizinische Physik, 29(2):86–101, 2019.
[2] Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, and Paul Kennedy. Deep learning techniques for medical image segmentation: achievements and challenges. Journal of digital imaging, 32:582–596, 2019.
[3] Eugene Vorontsov and Samuel Kadoury. Label noise in segmentation networks: mitigation must deal with bias. In DGM4MICCAI 2021 and DALI 2021, pages 251–258. Springer, 2021.
[4] Nicholas Heller, Joshua Dean, and Nikolaos Papanikolopoulos. Imperfect segmentation labels: How much do they matter? In CVII-STENT 2018 and LABELS 2018, pages 112–120. Springer, 2018.
[5] Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, and Daniel L Rubin. Ct-org, a new dataset for multiple organ segmentation in computed tomography. Scientific Data, 7(1):381, 2020