Deep Learning for Segmentation and Landmarking of the Human Anatomy: Applying nnU-Net and SpatialConfiguration-Net to Automate Statistical Shape Modeling for Sternum and Costal Cartilage
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
During vehicle development, simulations are used in part to replicate the mechanical
response of the human body during crash testing. To ensure realism, human body
models are employed. However, current models often lack an accurate representation
of thoracic anatomy, including the sternum and costal cartilage. One approach to
improve these models is to develop a statistical shape model based on computed
tomography (CT) scans of the thoracic region.
The creation of statistical shape models from CT data involves multiple steps and
is typically time-consuming. This study focuses on automating the first two steps
of the process: segmentation and landmarking, using two different deep learning
models. The aim is to enhance efficiency and consistency compared to manual
processing. Additionally, the impact of dataset size on the models’ performance is
investigated.
The creation of statistical shape models from CT data involves multiple steps and
is typically time-consuming. This study focuses on automating the first two steps
of the process: segmentation and landmarking using deep learning. The aim is to
enhance efficiency and consistency compared to manual processing. Additionally,
the impact of dataset size on model performance is investigated.
The methodology includes preparing datasets with manual segmentations and landmarks,
formatting them for use with deep learning models, and training two architectures:
nnU-Net for segmentation and SpatialConfiguration-Net (SCN) for landmarking.
The segmentation results from nnU-Net are generally accurate and more
consistent than manual segmentations, though some manual post-processing is still
necessary. SCN also demonstrates promising performance for landmarking, with
similar requirements for manual correction. Both models demonstrate improved
performance as the size of the training dataset increases. Additionally, nnU-Net’s
segmentation accuracy improves when trained on datasets with more precise annotations.
Despite the small dataset sizes (up to 10 scans for segmentation and 20 for
landmarking), both models achieved promising results, highlighting their robustness
even with limited data.
In conclusion, deep learning can effectively automate the segmentation and landmarking
processes required for statistical shape model development, offering improvements
in both efficiency and consistency. The findings also suggest that model
performance continues to improve with larger and more diverse datasets.
Beskrivning
Ämne/nyckelord
Deep Learning, Automatic Segmentation, Automatic Landmarking, nnUNet, SpatialConfiguration-Net, Medical Imaging, Thorax, Costal Cartilage, Sternum