4 research outputs found
Detecting respiratory motion artefacts for cardiovascular MRIs to ensure high-quality segmentation
While machine learning approaches perform well on their training domain, they
generally tend to fail in a real-world application. In cardiovascular magnetic
resonance imaging (CMR), respiratory motion represents a major challenge in
terms of acquisition quality and therefore subsequent analysis and final
diagnosis. We present a workflow which predicts a severity score for
respiratory motion in CMR for the CMRxMotion challenge 2022. This is an
important tool for technicians to immediately provide feedback on the CMR
quality during acquisition, as poor-quality images can directly be re-acquired
while the patient is still available in the vicinity. Thus, our method ensures
that the acquired CMR holds up to a specific quality standard before it is used
for further diagnosis. Therefore, it enables an efficient base for proper
diagnosis without having time and cost-intensive re-acquisitions in cases of
severe motion artefacts. Combined with our segmentation model, this can help
cardiologists and technicians in their daily routine by providing a complete
pipeline to guarantee proper quality assessment and genuine segmentations for
cardiovascular scans. The code base is available at
https://github.com/MECLabTUDA/QA_med_data/tree/dev_QA_CMRxMotion
Task-Agnostic Continual Hippocampus Segmentation for Smooth Population Shifts
Most continual learning methods are validated in settings where task boundaries are clearly defined and task identity information is available during training and testing. We explore how such methods perform in a task-agnostic setting that more closely resembles dynamic clinical environments with gradual population shifts. We propose ODEx, a holistic solution that combines out-of-distribution detection with continual learning techniques. Validation on two scenarios of hippocampus segmentation shows that our proposed method reliably maintains performance on earlier tasks without losing plasticity
Lifelong nnU-Net: a framework for standardized medical continual learning
Abstract As the enthusiasm surrounding Deep Learning grows, both medical practitioners and regulatory bodies are exploring ways to safely introduce image segmentation in clinical practice. One frontier to overcome when translating promising research into the clinical open world is the shift from static to continual learning. Continual learning, the practice of training models throughout their lifecycle, is seeing growing interest but is still in its infancy in healthcare. We present Lifelong nnU-Net, a standardized framework that places continual segmentation at the hands of researchers and clinicians. Built on top of the nnU-Net—widely regarded as the best-performing segmenter for multiple medical applications—and equipped with all necessary modules for training and testing models sequentially, we ensure broad applicability and lower the barrier to evaluating new methods in a continual fashion. Our benchmark results across three medical segmentation use cases and five continual learning methods give a comprehensive outlook on the current state of the field and signify a first reproducible benchmark