Automatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning

• URL: http://arxiv.org/abs/2102.03932v1
• Date: Sun, 7 Feb 2021 22:03:39 GMT
• Title: Automatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning
• Authors: Fazael Ayatollahi (1 and 2), Shahriar B. Shokouhi (1), Ritse M. Mann (2), Jonas Teuwen (2 and 3) ((1) Electrical Engineering Department, Iran University of Science and Technology (IUST), Tehran, Iran, (2) Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands, (3) Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands)
• Abstract summary: We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences. This method uses both the three-dimensional spatial information and temporal information obtained from the early-phase of the dynamic acquisition.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences. This method uses both the three-dimensional spatial information and temporal information obtained from the early-phase of the dynamic acquisition.Methods: The proposed CADe method, based on a modified 3D RetinaNet model, operates on ultrafast T1 weighted sequences, which are preprocessed for motion compensation, temporal normalization, and are cropped before passing into the model. The model is optimized to enable the detection of relatively small breast lesions in a screening setting, focusing on detection of lesions that are harder to differentiate from confounding structures inside the breast.Results: The method was developed based on a dataset consisting of 489 ultrafast MRI studies obtained from 462 patients containing a total of 572 lesions (365 malignant, 207 benign) and achieved a detection rate, sensitivity, and detection rate of benign lesions of 0.90, 0.95, and 0.86 at 4 false positives per normal breast with a 10-fold cross-validation, respectively.Conclusions: The deep learning architecture used for the proposed CADe application can efficiently detect benign and malignant lesions on ultrafast DCE-MRI. Furthermore, utilizing the less visible hard-to detect-lesions in training improves the learning process and, subsequently, detection of malignant breast lesions.

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