Annika Liebgott1,2, Louisa Fay1, Viet Chau Vu2, Bin Yang1, and Sergios Gatidis2
1Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 2Department of Radiology, University Hospital of Tuebingen, Tuebingen, Germany
1Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 2Department of Radiology, University Hospital of Tuebingen, Tuebingen, Germany
Immunotherapy is a promising approach to treat advanced malignant melanoma. However, not every patient responds to it, i.e. in the worst case crucial time is wasted. Our research hence focuses on methods to early assess individual therapy response from PET/MR images using deep learning models.
Figure 3:
Pipeline
of our deep
learning
framework. Preprocessing
of the data consists of organ segmentation, followed by data
normalization and creation of TFRecords for efficient data
processing, which are then split into training and test data and
resized such that all images have the same size. The
modular design allows to choose arbitrary deep learning models
to investigate different network architectures. Dashed
lines mark optional modules (data
augmentation, transfer learning). As data
augmentation, we implemented random rotation,
random shift and elastic
deformation9.
Figure 1: Exemplary abdominal slices
of one examination: images with fat (a) and water weighted (b) Dixon
sequences, ADC map (c) and PET image (d) were
acquired.
For
each patient, examinations
have been conducted
prior
to, two weeks and two months after starting immunotherapy.
