Abstract
Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is a diagnostic method suited for the early detection and diagnosis of cancer, involving the serial acquisition of images before and after the injection of a paramagnetic contrast agent. Dealing with long acquisition times, DCE-MRI inevitably shows noise (artefacts) in acquired images due to the patient (often involuntary) movements. As a consequence, over the years, machine learning approaches showed that some sort of motion correction technique (MCT) have to be applied in order to improve performance in tumours segmentation and classification. However, in recent times classic machine learning approaches have been outperformed by deep learning based ones, thanks to their ability to autonomously learn the best set of features for the task under analysis. This paper proposes a first investigation to understand if deep learning based approaches are more robust to the misalignment of images over time, making the registration no longer needed in this context. To this aim, we evaluated the effectiveness of a MCT both for the classification and for the segmentation of breast lesions in DCE-MRI by means of some literature proposal. Our results show that while MCTs seems to be still quite useful for the lesion segmentation task, they seem to be no longer strictly required for lesion classification one.
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Acknowledgments
The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research, the availability of the Calculation Centre SCoPE of the University of Naples Federico II and thank the SCoPE academic staff for the given support. The authors are also grateful to Dr. Antonella Petrillo, Head of Division of Radiology and PhD Roberta Fusco, Department of Diagnostic Imaging, Radiant and Metabolic Therapy, “Istituto Nazionale dei Tumori Fondazione G. Pascale” - IRCCS, Naples, Italy, for providing data. This work is part of the “Synergy-net: Research and Digital Solutions against Cancer” project (funded in the framework of the POR Campania FESR 2014–2020 - CUP B61C17000090007).
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Galli, A., Gravina, M., Marrone, S., Piantadosi, G., Sansone, M., Sansone, C. (2019). Evaluating Impacts of Motion Correction on Deep Learning Approaches for Breast DCE-MRI Segmentation and Classification. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_26
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