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

@article{Ayatollahi2021AutomaticBL,
  title={Automatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning},
  author={Fazael Ayatollahi and Shahriar Baradaran Shokouhi and Ritse M. Mann and Jonas Teuwen},
  journal={Medical physics},
  year={2021}
}
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… 

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References

SHOWING 1-10 OF 34 REFERENCES

Fully automated detection of breast cancer in screening MRI using convolutional neural networks

A CADe system that is able to exploit the spatial information obtained from the early-phase scans and can be used in screening programs where abbreviated MRI protocols are used is developed.

Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI

The deep Q-network approach is extended, previously demonstrated on simpler problems such as anatomical landmark detection, in order to detect lesions that have a significant variation in shape, appearance, location and size.

Automated Characterization of Breast Lesions Imaged With an Ultrafast DCE-MR Protocol

Novel features extracted from the kinetics of contrast agent uptake imaged by a short (100 s) view-sharing MRI protocol are introduced, and how these features measure up to commonly used features for regular DCE-MRI of the breast is investigated.

Automated localization of breast cancer in DCE-MRI

Hybrid Mass Detection in Breast MRI Combining Unsupervised Saliency Analysis and Deep Learning

A hybrid mass-detection algorithm that combines unsupervised candidate detection with deep learning-based classification and a novel multi-channel image representation is described, which achieved higher classification performance compared to single-channel images.

Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics

This work proposes a mask-guided hierarchical learning (MHL) framework for breast tumor segmentation via fully convolutional networks (FCN), and develops an FCN model to generate a 3D breast mask as the region of interest (ROI) for each image, to remove confounding information from input DCE-MR images.

A computer-aided diagnosis system for breast DCE-MRI at high spatiotemporal resolution.

A computer-aided diagnosis system for characterization of breast lesions imaged with high spatiotemporal resolution DCE-MRI of the breast which outperforms a previously proposed system in classifying benign and malignant lesions, while it requires less user interactions.

Artificial Intelligence–Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI

Use of adjunct imaging and PI has a significant contribution in diagnostic performance of ultrafast breast MRI and the developed AI system for interpretation of multiparametric ultra fast breast MRI may improve specificity.

Differentiating benign and malignant mass and non-mass lesions in breast DCE-MRI using normalized frequency-based features

A new computer-aided diagnosis (CADx) to distinguish between malign and benign mass and non-mass lesions in breast DCE-MRI is proposed and the combination of normalized frequency-based features and three-dimensional shape descriptors improves the CADx performance.