Asymmetric Ensemble of Asymmetric U-Net Models for Brain Tumor Segmentation With Uncertainty Estimation

Deep learning-based automated scan plane positioning for brain magnetic resonance imaging

Background

Manual planning of scans in clinical magnetic resonance imaging (MRI) exhibits poor accuracy, lacks consistency, and is time-consuming. Meanwhile, classical automated scan plane positioning methods that rely on certain assumptions are not accurate or stable enough, and are computationally inefficient for practical application scenarios. This study aims to develop and evaluate an effective, reliable, and accurate deep learning-based framework that incorporates prior physical knowledge for automatic head scan plane positioning in MRI.

Methods

A deep learning-based end-to-end automated scan plane positioning framework has been developed for MRI head scans. Our model takes a three-dimensional (3D) pre-scan image input, utilizing a cascaded 3D convolutional neural network to detect anatomical landmarks from coarse to fine. And then, with the determined landmarks, accurate scan plane localization can be achieved. A multi-scale spatial information fusion module was employed to aggregate high- and low-resolution features, combined with physically meaningful point regression loss (PRL) function and direction regression loss (DRL) function. Meanwhile, we simulate complex clinical scenarios to design data augmentation strategies.

Results

Our proposed approach shows good performance on a clinically wide range of 229 MRI head scans, with a point-to-point absolute error (PAE) of 0.872 mm, a point-to-point relative error (PRE) of 0.10%, and an average angular error (AAE) of 0.502°, 0.381°, and 0.675° for the sagittal, transverse, and coronal planes, respectively.

Conclusions

The proposed deep learning-based automated scan plane positioning shows high efficiency, accuracy and robustness when evaluated on varied clinical head MRI scans with differences in positioning, contrast, noise levels and pathologies.

A review on brain tumor segmentation based on deep learning methods with federated learning techniques

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues.