Hannah Strohm, Sven Rothlübbers, Jürgen Jenne, Dirk-André Clevert, Thomas Fischer, Niklas Hitschrich, Bernhard Mumm, Paul Spiesecke, Matthias Günther
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引用次数: 0
Abstract
Purpose: Contrast-enhanced ultrasound (CEUS) is a reliable tool to diagnose focal liver lesions, which appear ambiguous in normal B-mode ultrasound. However, interpretation of the dynamic contrast sequences can be challenging, hindering the widespread application of CEUS. We investigate the use of a deep-learning-based image classifier for determining the diagnosis-relevant feature washout from CEUS acquisitions.
Approach: We introduce a data representation, which is agnostic to data heterogeneity regarding lesion size, subtype, and length of the sequences. Then, an image-based classifier is exploited for washout classification. Strategies to cope with sparse annotations and motion are systematically evaluated, as well as the potential benefits of using a perfusion model to cover missing time points.
Results: Results indicate decent performance comparable to studies found in the literature, with a maximum balanced accuracy of 84.0% on the validation and 82.0% on the test set. Correlation-based frame selection yielded improvements in classification performance, whereas further motion compensation did not show any benefit in the conducted experiments.
Conclusions: It is shown that deep-learning-based washout classification is feasible in principle. It offers a simple form of interpretability compared with benign versus malignant classifications. The concept of classifying individual features instead of the diagnosis itself could be extended to other features such as the arterial inflow behavior. The main factors distinguishing it from existing approaches are the data representation and task formulation, as well as a large dataset size with 500 liver lesions from two centers for algorithmic development and testing.
期刊介绍:
JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.