Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation.

Contrast and texture modifications applied during training or test-time have recently shown promising results to enhance the generalization performance of deep learning segmentation methods in medical image analysis. However, a deeper understanding of this phenomenon has not been investigated. In this study, we investigated this phenomenon using a controlled experimental setting, using datasets from the Human Connectome Project and a large set of simulated MR protocols, in order to mitigate data confounders and investigate possible explanations as to why model performance changes when applying different levels of contrast and texture-based modifications. Our experiments confirm previous findings regarding the improved performance of models subjected to contrast and texture modifications employed during training and/or testing time, but further show the interplay when these operations are combined, as well as the regimes of model improvement/worsening across scanning parameters. Furthermore, our findings demonstrate a spatial attention shift phenomenon of trained models, occurring for different levels of model performance, and varying in relation to the type of applied image modification

Unsupervised Lesion Detection via Image Restoration with a Normative Prior

Unsupervised lesion detection is a challenging problem that requires accurately estimating normative distributions of healthy anatomy and detecting lesions as outliers without training examples. Recently, this problem has received increased attention from the research community following the advances in unsupervised learning with deep learning. Such advances allow the estimation of high-dimensional distributions, such as normative distributions, with higher accuracy than previous methods.The main approach of the recently proposed methods is to learn a latent-variable model parameterized with networks to approximate the normative distribution using example images showing healthy anatomy, perform prior-projection, i.e. reconstruct the image with lesions using the latent-variable model, and determine lesions based on the differences between the reconstructed and original images. While being promising, the prior-projection step often leads to a large number of false positives. In this work, we approach unsupervised lesion detection as an image restoration problem and propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation. The probabilistic model punishes large deviations between restored and original images, reducing false positives in pixel-wise detections. Experiments with gliomas and stroke lesions in brain MRI using publicly available datasets show that the proposed approach outperforms the state-of-the-art unsupervised methods by a substantial margin, +0.13 (AUC), for both glioma and stroke detection. Extensive model analysis confirms the effectiveness of MAP-based image restoration.Comment: Extended version of 'Unsupervised Lesion Detection via Image Restoration with a Normative Prior' (MIDL2019

SaRF: Saliency regularized feature learning improves MRI sequence classification.

BACKGROUND AND OBJECTIVE Deep learning based medical image analysis technologies have the potential to greatly improve the workflow of neuro-radiologists dealing routinely with multi-sequence MRI. However, an essential step for current deep learning systems employing multi-sequence MRI is to ensure that their sequence type is correctly assigned. This requirement is not easily satisfied in clinical practice and is subjected to protocol and human-prone errors. Although deep learning models are promising for image-based sequence classification, robustness, and reliability issues limit their application to clinical practice. METHODS In this paper, we propose a novel method that uses saliency information to guide the learning of features for sequence classification. The method uses two self-supervised loss terms to first enhance the distinctiveness among class-specific saliency maps and, secondly, to promote similarity between class-specific saliency maps and learned deep features. RESULTS On a cohort of 2100 patient cases comprising six different MR sequences per case, our method shows an improvement in mean accuracy by 4.4% (from 0.935 to 0.976), mean AUC by 1.2% (from 0.9851 to 0.9968), and mean F1 score by 20.5% (from 0.767 to 0.924). Furthermore, based on feedback from an expert neuroradiologist, we show that the proposed approach improves the interpretability of trained models as well as their calibration with reduced expected calibration error (by 30.8%, from 0.065 to 0.045). The code will be made publicly available. CONCLUSIONS In this paper, the proposed method shows an improvement in accuracy, AUC, and F1 score, as well as improved calibration and interpretability of resulting saliency maps

Unsupervised Lesion Detection via Image Restoration with a Normative Prior

ISSN:2640-349

Unsupervised lesion detection via image restoration with a normative prior

ISSN:1361-8415ISSN:1361-842