Abstract
Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be challenging, time-consuming and subject to observer bias. Automated detection methods often need voxel-wise annotations for training. We propose a novel approach for automated lesion detection that can be trained on scans only annotated with a dot per lesion instead of a full segmentation. From the dot annotations and their corresponding intensity images we compute various distance maps (DMs), indicating the distance to a lesion based on spatial distance, intensity distance, or both. We train a fully convolutional neural network (FCN) to predict these DMs for unseen intensity images. The local optima in the predicted DMs are expected to correspond to lesion locations. We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans. Our method matches the intra-rater performance of the expert rater that was computed on an independent set. We compare the different types of distance maps, showing that incorporating intensity information in the distance maps used to train an FCN greatly improves performance.
K.M.H. van Wijnen and F. Dubost—Both authors contributed equally to this work.
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Notes
- 1.
Our code for computing 2D as well as 3D distance maps is available at https://github.com/kimvwijnen/geodesic_distance_transform.
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Acknowledgments
This research is part of the research project Deep Learning for Medical Image Analysis (DLMedIA) with project number P15-26, funded by the Dutch Technology Foundation STW (part of the Netherlands Organisation for Scientific Research (NWO), which is partly funded by the Ministry of Economic Affairs), and with co-financing by Quantib. This research was also funded by the Netherlands Organisation for Health Research and Development (ZonMw), Project 104003005. Part of this work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative and on a Quadro P6000 donated by the NVIDIA Corporation.
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van Wijnen, K.M.H. et al. (2019). Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_26
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