Fully Automated Segmentation of Subcutaneous Fat in CT Images

Type: MA thesis

Status: running

Date: June 1, 2022 - December 1, 2022

Supervisors: Felix Denzinger, Leonhard Rist, Felix Durlak, Andreas Maier

Thesis description

Given that obesity is as a major global health issue, as well as the fact that body fat is an important
risk factor for cancer, many cardiovascular and metabolic diseases [1, 2], providing a precise measuring
tool for the distribution of adipose tissue is of high interest. Adipose tissue is also associated with
many physiological functions as the principal energy storage organ and due to its endocrine activity [3].
Computed tomography (CT) and magnetic resonance imaging (MRI) are both utilized to localize and
quantify body fat, however, in contrast to CT, MRI is more challenging with the current segmentation
problem because of the image intensity inhomogeneities [4]. In addition, MRI is slower, more expensive
and thus less clinically available [5].

To the best of our knowledge, all approaches of the most recent publications addressing the problem
of subcutaneous adipose tissue (SAT) segmentation still lack one or more important aspects. The vast
majority of related approaches are only semi-automatic, requiring a carefully chosen user input to
reach their goals [6] or are rather mostly manual [1, 7, 8]. Several convolutional neural network-based
methods [9, 10], together with an active-contour-based method [11] for fully automatic subcutaneous
fat segmentation were already proposed, but they are either limited to the abdominal region or are
only operating on 2D image slices, which is sub-optimal for 3D image data. A novel neural network
architecture was found to have achieved accurate 3D segmentation results on volumetric CT data for
both thorax and abdomen [5]. However, the remaining drawbacks are the mislabeled annotations for
certain thoracic slices and the relatively small training dataset (only 18 images), which will restrict
the model generalizability. Therefore, the contribution of this thesis is intended to fill the gaps in the
previous publications by introducing a fully automatic, more reliable and reproducible framework for
3D segmentation of the abdominal and thoracic SAT in CT images.

Semantic segmentation networks have become a powerful tool for segmenting spatially structured
images and thus play an essential role for biomedical image data. However, since there is no dataset
available with the required ground truth annotations of SAT, these annotation masks have to be
generated first. Manual delineation of the inner and outer contours that are defining SAT on axial
images is an inefficient and time-consuming process, so some semi-automatic algorithms are used to
accelerate the generation of initial segmentation masks for our dataset. The dataset originally consists
of selected CT images from Siemens internal database. In principle, the work of the thesis will be
twofold. In the first phase, active contours (AC) [12, 13] will be used as a baseline algorithm for SAT
segmentation. Nevertheless, several improvement steps, such as finding optimum AC parameters,
helpful preprocessing and having consistent 3D masks [14], need to be implemented to get satisfactory
segmentations. Final annotations can be then used for the training after some manual corrections.
The second phase is to train a deep neural network using the annotated dataset in order to have a
fully automatic 3D SAT segmentation. For this task, nnU-Net [15] as an advanced state-of-the-art
deep learning-based segmentation tool is going to be applied. Furthermore, different training schemes
that rely on anatomical prior knowledge (i.e. two different segmentation networks for thorax and
abdomen) and ground truth-driven patch sampling will be implemented and evaluated.

The thesis will comprise the following work items:
• Literature review of most efficient active contour segmentation algorithms, fully- and semiautomated
segmentation methods for subcutaneous fat in CT images
• Utilization of the improved active contour algorithms besides some manual corrections to generate
an annotated dataset
• Implementation and training of a deep neural network to fully automate SAT segmentation
• Quantitative assessment and evaluation of the developed method
• Encapsulation of the new pipeline into usable MeVisLab modules (www.mevislab.de) for later
development of the company’s current prototype software

 

References

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