Population-based analysis of medical images plays an essential role in identification and development of imaging biomarkers. Most commonly the focus lies on a single structure or image region in order to identify variations to discriminate between patient groups. Such approaches require high segmentation accuracy in specific image regions while the accuracy in the remaining image area is of less importance. We propose an efficient ROI-based approach for unsupervised learning of deformable atlas-to-image registration to facilitate structure-specific analysis. Our hierarchical model improves registration accuracy in relevant image regions while reducing computational cost in terms of memory consumption, computation time and consequently energy consumption. The proposed method was evaluated for predicting cognitive impairment from morphological changes of the hippocampal region in brain MRI images showing that next to the efficient processing of 3D data, our method delivers accurate results comparable to state-of-the-art tools.
In this work, a generative adversarial network (GAN)-based pipeline for the generation of realistic retinal optical coherence tomography (OCT) images with available pathological structures and ground truth anatomical and pathological annotations is established. The emphasis of the proposed image generation approach lies especially on the simulation of the pathology-induced deformations of the retinal layers around a pathological structure. Our experiments demonstrate the realistic appearance of the images as well as their applicability for the training of neural networks.
The growing popularity of black box machine learning methods for medical image analysis makes their interpretability to a crucial task. To make a system, e.g. a trained neural network, trustworthy for a clinician, it needs to be able to explain its decisions and predictions. In this work, we tackle the problem of generating plausible explanations for the predictions of medical image classifiers, that are trained to differentiate between different types of pathologies and healthy tissues. An intuitive solution to determine which image regions influence the trained classifier is to find out whether the classification results change when those regions are deleted. This idea can be formulated as a minimization problem and thus efficiently implemented. However, the meaning of “deletion” of image regions, in our case pathologies in medical images, is not defined. We contribute by defining the deletion of pathologies, as the replacement by their healthy looking equivalent generated using variational autoencoders. The experiments with a classification neural network on OCT (Optical Coherence Tomography) images and brain lesion MRIs show that a meaningful replacement of “deleted” image regions has significant impact on the reliability of the generated explanations. The proposed deletion method is proven to be successful since our approach delivers the best results compared to four other established methods.
Conference Committee Involvement (1)
Imaging Informatics
17 February 2025 | San Diego, California, United States
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.