Abstract
Chronic active multiple sclerosis lesions, also referred to as rim+ lesions, are characterized by a hyperintense rim observed at the lesion’s edge on quantitative susceptibility maps. Despite their geometrically simple structure, characterized by radially oriented gradients at the lesion edge with a greater gradient magnitude compared to non-rim+ (rim-) lesions, recent studies indicate that the identification performance for these lesions is subpar due to limited data and significant class imbalance. In this paper, we propose a simple yet effective image processing operation, deep directed accumulator (DeDA), which provides a new perspective for injecting domain-specific inductive biases (priors) into neural networks for rim+ lesion identification. Given a feature map and a set of sampling grids, DeDA creates and quantizes an accumulator space into finite intervals and accumulates corresponding feature values. This DeDA operation can be regarded as a symmetric operation to the grid sampling within the forward-backward neural network framework, the process of which is order-agnostic, and can be efficiently implemented with the native CUDA programming. Experimental results on a dataset with 177 rim+ and 3986 rim- lesions show that \(10.1\%\) of improvement in a partial (false positive rate \(<0.1\)) area under the receiver operating characteristic curve (pROC AUC) and \(10.2\%\) of improvement in an area under the precision recall curve (PR AUC) can be achieved respectively comparing to other state-of-the-art methods. The source code is available online at https://github.com/tinymilky/DeDA.
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Acknowledgement
The database was approved by the local Institutional Review Board and written informed consent was obtained from all patients prior to their entry into the database. We would like to thank folks from Weill Cornell for sharing the data used in this paper.
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Zhang, H., Wang, R., Hu, R., Zhang, J., Li, J. (2023). DeDA: Deep Directed Accumulator. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14221. Springer, Cham. https://doi.org/10.1007/978-3-031-43895-0_72
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DOI: https://doi.org/10.1007/978-3-031-43895-0_72
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