Large-scale population studies have examined the detection of sinus opacities in cranial MRIs. Deep learning methods, specifically 3D convolutional neural networks (CNNs), have been used to classify these anomalies. However, CNNs have limitations in capturing long-range dependencies across the low and high level features, potentially reducing performance. To address this, we propose an end-to-end pipeline using a novel deep learning network called ConTra-Net. ConTra-Net combines the strengths of CNNs and self-attention mechanisms of transformers to classify paranasal anomalies in the maxillary sinuses. Our approach outperforms 3D CNNs and 3D Vision Transformer (ViT), with relative improvements in F1 score of 11.68% and 53.5%, respectively. Our pipeline with ConTra-Net could serve as an alternative to reduce misdiagnosis rates in classifying paranasal anomalies.
Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techniques to distinguish between normal and abnormal samples. This method limits the type of anomalies that can be classified as the anomalies need to be present in the training data. Further, many data points from normal and anomaly class are needed for the model to achieve satisfactory classification performance. However, experienced clinicians can segregate between normal samples (healthy maxillary sinus) and anomalous samples (anomalous maxillary sinus) after looking at a few normal samples. We mimic the clinicians ability by learning the distribution of healthy maxillary sinuses using a 3D convolutional auto-encoder (cAE) and its variant, a 3D variational autoencoder (VAE) architecture and evaluate cAE and VAE for this task. Concretely, we pose the paranasal anomaly detection as an unsupervised anomaly detection problem. Thereby, we are able to reduce the labelling effort of the clinicians as we only use healthy samples during training. Additionally, we can classify any type of anomaly that differs from the training distribution. We train our 3D cAE and VAE to learn a latent representation of healthy maxillary sinus volumes using L1 reconstruction loss. During inference, we use the reconstruction error to classify between normal and anomalous maxillary sinuses. We extract sub-volumes from larger head and neck MRIs and analyse the effect of different fields of view on the detection performance. Finally, we report which anomalies are easiest and hardest to classify using our approach. Our results demonstrate the feasibility of unsupervised detection of paranasal anomalies from MRIs with an AUPRC of 85% and 80% for cAE and VAE, respectively.
DeepMedic, an open source software library based on a multi-channel multi-resolution 3D convolutional neural network, has recently been made publicly available for brain lesion segmentations. It has already been shown that segmentation tasks on MRI data of patients having traumatic brain injuries, brain tumors, and ischemic stroke lesions can be performed very well. In this paper we describe how it can efficiently be used for the purpose of detecting and segmenting white matter hyperintensity lesions. We examined if it can be applied to single-channel routine 2D FLAIR data. For evaluation, we annotated 197 datasets with different numbers and sizes of white matter hyperintensity lesions. Our experiments have shown that substantial results with respect to the segmentation quality can be achieved. Compared to the original parametrization of the DeepMedic neural network, the timings for training can be drastically reduced if adjusting corresponding training parameters, while at the same time the Dice coefficients remain nearly unchanged. This enables for performing a whole training process within a single day utilizing a NVIDIA GeForce GTX 580 graphics board which makes this library also very interesting for research purposes on low-end GPU hardware.
Stroke is a leading cause of death and disability in the western hemisphere. Acute ischemic strokes can be broadly classified based on the underlying cause into atherosclerotic strokes, cardioembolic strokes, small vessels disease, and stroke with other causes. The ability to determine the exact origin of an acute ischemic stroke is highly relevant for optimal treatment decision and preventing recurrent events. However, the differentiation of atherosclerotic and cardioembolic phenotypes can be especially challenging due to similar appearance and symptoms. The aim of this study was to develop and evaluate the feasibility of an image-based machine learning approach for discriminating between arteriosclerotic and cardioembolic acute ischemic strokes using 56 apparent diffusion coefficient (ADC) datasets from acute stroke patients. For this purpose, acute infarct lesions were semi-atomically segmented and 30,981 geometric and texture image features were extracted for each stroke volume. To improve the performance and accuracy, categorical Pearson’s χ2 test was used to select the most informative features while removing redundant attributes. As a result, only 289 features were finally included for training of a deep multilayer feed-forward neural network without bootstrapping. The proposed method was evaluated using a leave-one-out cross validation scheme. The proposed classification method achieved an average area under receiver operator characteristic curve value of 0.93 and a classification accuracy of 94.64%. These first results suggest that the proposed image-based classification framework can support neurologists in clinical routine differentiating between atherosclerotic and cardioembolic phenotypes.
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