MixMicrobleedNet: segmentation of cerebral microbleeds using nnU-Net

Cerebral microbleeds are small hypointense lesions visible on magnetic resonance imaging (MRI) with gradient echo, T2*, or susceptibility weighted (SWI) imaging. Assessment of cerebral microbleeds is mostly performed by visual inspection. The past decade has seen the rise of semi-automatic tools to assist with rating and more recently fully automatic tools for microbleed detection. In this work, we explore the use of nnU-Net as a fully automated tool for microbleed segmentation. Data was provided by the ``Where is VALDO?'' challenge of MICCAI 2021. The final method consists of nnU-Net in the ``3D full resolution U-Net'' configuration trained on all data (fold = `all'). No post-processing options of nnU-Net were used. Self-evaluation on the training data showed an estimated Dice of 0.80, false discovery rate of 0.16, and false negative rate of 0.15. Final evaluation on the test set of the VALDO challenge is pending. Visual inspection of the results showed that most of the reported false positives could be an actual microbleed that might have been missed during visual rating. Source code is available at: https://github.com/hjkuijf/MixMicrobleedNet . The docker container hjkuijf/mixmicrobleednet can be pulled from https://hub.docker.com/r/hjkuijf/mixmicrobleednet

Effect of latent space distribution on the segmentation of images with multiple annotations

We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the variation in the reference segmentations for lung tumors and white matter hyperintensities in the brain. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNetComment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:005. arXiv admin note: text overlap with arXiv:2207.1287

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.Comment: Submitted for publication. Comments welcome by email to first autho

Bringing AI to the clinic: blueprint for a vendor-neutral AI deployment infrastructure

AI provides tremendous opportunities for improving patient care, but at present there is little evidence of real-world uptake. An important barrier is the lack of well-designed, vendor-neutral and future-proof infrastructures for deployment. Because current AI algorithms are very narrow in scope, it is expected that a typical hospital will deploy many algorithms concurrently. Managing stand-alone point solutions for all of these algorithms will be unmanageable. A solution to this problem is a dedicated platform for deployment of AI. Here we describe a blueprint for such a platform and the high-level design and implementation considerations of such a system that can be used clinically as well as for research and development. Close collaboration between radiologists, data scientists, software developers and experts in hospital IT as well as involvement of patients is crucial in order to successfully bring AI to the clinic

Calibration techniques for node classification using graph neural networks on medical image data

Miscalibration of deep neural networks (DNNs) can lead to unreliable predictions and hinder their use in clinical decision-making. This miscalibration is often caused by overconfident probability estimates. Calibration techniques such as model ensembles, regularization terms, and post-hoc scaling of the predictions can be employed to improve the calibration performance of DNNs. In contrast to DNNs, graph neural networks (GNNs) tend to exhibit underconfidence. In this study, we investigate the efficacy of calibration techniques developed for DNNs when applied to GNNs trained on medical image data, and compare the calibration performance of binary and multiclass node classification on a benchmark dataset and a medical image dataset. We find that post-hoc methods using Platt scaling or Temperature scaling, or methods that add a regularization term to the loss function during training are most effective to improve calibration. Our results further indicate that these calibration techniques are more effective for multiclass classification tasks compared to binary classification tasks

MixMicrobleed: Multi-stage detection and segmentation of cerebral microbleeds

Cerebral microbleeds are small, dark, round lesions that can be visualised on T2*-weighted MRI or other sequences sensitive to susceptibility effects. In this work, we propose a multi-stage approach to both microbleed detection and segmentation. First, possible microbleed locations are detected with a Mask R-CNN technique. Second, at each possible microbleed location, a simple U-Net performs the final segmentation. This work used the 72 subjects as training data provided by the "Where is VALDO?" challenge of MICCAI 2021

Subvoxel vessel wall thickness measurements of the intracranial arteries using a convolutional neural network

Vessel wall thickening of the intracranial arteries has been associated with cerebrovascular disease and atherosclerotic plaque development. Visualization of the vessel wall has been enabled by recent advancements in vessel wall MRI. However, quantifying early wall thickening from these MR images is difficult and prone to severe overestimation, because the voxel size of clinically used acquisitions exceeds the wall thickness of the intracranial arteries. In this study, we aimed for accurate and precise subvoxel vessel wall thickness measurements. A convolutional neural network was trained on MR images of 34 ex vivo circle of Willis specimens, acquired with a clinically used protocol (isotropic acquired voxel size: 0.8 mm). Ground truth measurements were performed on images acquired with an ultra-high-resolution protocol (isotropic acquired voxel size: 0.11 mm) and were used for evaluation. Additionally, we determined the robustness of our method by applying Monte Carlo dropout and test time augmentation. Lastly, we applied our method on in vivo images of three intracranial aneurysms to measure their wall thickness. Our method shows resolvability of different vessel wall thicknesses, well below the acquired voxel size. The method described may facilitate quantitative measurements on MRI data for a wider range of clinical applications