Index
Lung Nodule Classification in CT Images using Deep Learning
Development of a Fast Biomechanical Cardiac Model for the Treatment Planning of Dilated Cardiomyopathy
Automatic Deep Learning Lung Lesion Characterization with Combined Application of State-of-the-Art Transfer Learning and Image Augmentation Techniques
Cephalometric Landmark Re-annotation and Automatic Detection
Solution to Extend the Field of View of Computed Tomography Using Deep Learning Approaches
Incorporating Time Series Information into Glacier Segmentation and Front Detection using U-Nets in Combination with LSTMs and Multi-Task Learning
This thesis aims at integrating time series information into the static segmentation of glaciers and their
calving fronts in synthetic aperture radar (SAR) image sequences. U-Nets have recently been shown
to provide promising results for glacier (front) segmentation using synthetic aperture radar (SAR)
imagery [1]. However, this approach only incorporates the spatial information in a single image. The
temporal information of complete image sequences, each showing one glacier at different time points,
has not been addressed thus far. To fill this gap two approaches shall be worked on:
- approach 1; using Long Short-Term Memory (LSTM) layers in the U-Net architecture:
Recurrent Neural Networks like LSTMs are designed such that information from previous
inputs in a sequence can be stored in a memory and used to ameliorate the prediction for
the current input. The combination of structured LSTMs and Fully Convolutional Networks
(FCNs) showed promising results for joint 4D segmentation of longitudinal MRI [2]. In [3], a
U-Net was successfully combined with a bi-directional convolutional LSTM for aortic image
sequence segmentation outperforming a simple U-Net in segmentation accuracy. In this thesis,
the combination of LSTMs and U-Nets will be tested for glacier segmentation and calving
front detection in SAR image sequences. Moreover, the use of Recurrent layers (RNN), Gated
Recurrent Units (GRU) and bi-directional LSTMS instead of simple LSTMs shall be investigated
as well. - approach 2; Multi-Task Learning (MLT): As the region to be segmented for calving front
detection is a small part of the image, this task shows a severe class-imbalance. To improve its
performance, an MLT approach shall be implemented jointly training glacier segmentation and
calving front detection. Performance enhancement of U-Nets have been observed using stacking
[4] and shared encoding networks [5, 6]. In this thesis, both MLT techniques shall be tested
using U-Nets in combination with LSTMs (see point 1).
The resulting models will be compared quantitatively and qualitatively with the state-of-the-art and
shall be implemented in Keras.
[1] Zhang et al. “Automatically delineating the calving front of Jakobshavn Isbræ from multitemporal
TerraSAR-X images: a deep learning approach.” The Cryosphere 13, no. 6 (2019): 1729-1741.
[2] Gao et al. “Fully convolutional structured LSTM networks for joint 4D medical image segmentation.”
In: IEEE 15th International Symposium on Biomedical Imaging, Washington, DC, 2018, IEEE, pp.
1104-1108.
[3] Bai et al. “Recurrent Neural Networks for Aortic Image Sequence Segmentation with Sparse Annotations.”
In Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-L´opez, Gabor Fichtinger
(Eds.): Medical Image Computing and Computer Assisted Intervention – MICCAI, 2018, pp. 586-594.
[4] Sun et al. “Stacked U-Nets with Multi-Output for Road Extraction.” In: CVPR Workshops, Salt Lake
City, 2018, pp. 202-206.
[5] Ke et al. “Learning to segment microscopy images with lazy labels.” In: ECCV Workshop on BioImage
Computing, 2020.
[6] Lee et al. “Multi-Task Learning U-Net for Single-Channel Speech Enhancement and Mask-Based Voice
Activity Detection.” Applied Sciences 10, no. 9 (2020): p. 3230.
torchsense – a PyTorch-based Compressed Sensing reconstruction framework for dynamic MRI
In this master thesis a novel deep learning-based reconstruction method specifically tailored for cardiac radial cine MRI image sequences is investigated. Despite the many advantages presented by state-of-the-art unrolled networks, their applicability is limited due to integration of the forward operator into the scheme which poses a computational challenge within the scope of dynamic non-Cartesian MRI. The novelty of our algorithm constitutes the decoupling of regularization and data consistency enforcement into two separate steps that can be combined into an end-to-end reconstruction scheme which reduces the usage of the forward operator and, thereby, offers more flexibility. In contrast to unrolled networks, the regularization step will be achieved by a lightweight denoising CNN, in some cases leading to a closed-form solution of the data-consistency step.
Utilizing the flexibility (e.g., variable network length at test time), we will seek to increase the undersampling ratio of the k-space, thereby, allowing a higher temporal resolution using an existing acquisition scheme.
Automatic segmentation of whole heart
Congenital Disease (CD) are defects that exist in newborn babies. Neural tube defects, craniofacial
anomalies, congenital heart diseases (CHD) are some of them and amongst them, Congenital Heart
Diseases are the most common type of anomalies that a ect 4 to 50 per 1000 infants based on the
di erence in demographic characteristics and experiment conditions [1].
Medical Image segmentation is one of the most important parts of planning the steps of treatment
for patients with CHD. Image segmentation techniques aim to detect boundaries within a 2D or 3D
image and partition the image into meaningful parts based on pixel level information e.g. intensity
value and spatial information e.g. anatomical knowledge [3]. However, segmentation for a single 3D
medical image might take some hours. In addition to that, the complexity of images and the fact that
understanding these images needs medical expertise make them costly to annotate which makes an
automatic segmentation framework crucial.
Previously an interactive segmentation method is suggested for this purpose [2]. This master
thesis aims to reduce the manual interaction of the users by investigating di erent machine learning
approaches to nd a highly accurate model that could potentially replace the interactive solution.
The thesis has to comprise the following work items:
• Literature overview of state-of-the-art segmentation methods, particularly deep learning meth-
ods, for 3D medical images.
• Implementation and training of di erent deep learning segmentation models.
• Evaluation of trained models based on dice score and comparing them to previous interactive
approaches.
References
[1] Manuel Giraldo-Grueso, Ignacio Zarante, Alejandro Meja-Grueso, and Gloria Gracia. Risk factors
for congenital heart disease: A case-control study. Revista Colombiana de Cardiologa, 27(4):324{
329, 2020.
[2] Danielle F Pace. Image segmentation for highly variable anatomy: applications to congenital heart
disease. PhD thesis, Massachusetts Institute of Technology, 2020.
[3] Felix Renard, Soulaimane Guedria, Noel De Palma, and Nicolas Vuillerme. Variability and repro-
ducibility in deep learning for medical image segmentation. Scienti c Reports, 10(1):1{16, 2020.
Automatic Bird Individual Recognition in Multi-Channel Recording Scenarios
Problem background:
At the Max-Planck-Institute for Ornithology in Radolfszell several birds are equipped with
backpacks to record their calls. But not only the sound of the equipped bird is recorded but also
of the birds in its surroundings and as a result the scientists receive several non-synchronous
audio tracks with bird calls. The biologists have to manually match the calls to the individual
birds, which is time-consuming and can easily lead to mistakes.
Goal of the thesis:
The goal of this thesis is to implement a python framework that can assign the calls to the
corresponding birds.
Since the intensity of a call decreases exponentially with distance, the loudest call can be
matched to the bird with this recorder. Also, the call of the mentioned bird appears earlier on
its own recording device than on the other devices.
To assign the further calls to the remaining birds, the soundtracks must be compared by
overlaying the audio signals. For this purpose, the audio signals have to be modified first:
Since different devices are used for capturing data and because the recordings cannot be started
at the same time, a linear time offset between the recordings occurs. Also, a linear distortion
appears as the devices record at different frequencies.
To remove these inconsistencies, similar characteristics must be found in the audio signals and
then the audio tracks have to be shifted and processed until these characteristics lie one above
another. There are several methods to filter out these characteristics, whereby the most precise
methods require human assistance [1]. But there are also some automated approaches, where
the audio track is scanned for periodic signal parameters such as pitch or spectral flatness.
Effective features are essential for the removal of distortion as well as a good ability of the
algorithm to distinguish between minor similarities of the characteristics [2].
The framework will be implemented in Python. It should process the given audio tracks and
recognize and reject disturbed channels.
References:
[1] Brett G. Crockett, Michael J. Smithers. Method for time aligning audio signals using
characterizations based on auditory events, 2002
[2] Jürgen Herre, Eric Allamanche, Oliver Hellmuth. Robust matching of audio signals using
spectral flatness features, 2002