Magnetic Resonance Imaging Organ Research Articles

Magnetic resonance imaging organ at risk delineation for nasopharyngeal radiotherapy: Measuring the effectiveness of an educational intervention.

Magnetic resonance imaging (MRI) demonstrates superior soft tissue contrast and is increasingly being used in radiotherapy planning. This study evaluated the impact of an education workshop in minimising inter-observer variation (IOV) for nasopharyngeal organs at risk (OAR) delineation on MRI. Ten observers delineated 14 OARs on 4 retrospective nasopharyngeal MRI data sets. Standard contouring guidelines were provided pre-workshop. Following an education workshop on MRI OAR delineation, observers blinded to their original contours repeated the 14 OAR delineations. For comparison, reference volumes were delineated by two head and neck radiation oncologists. IOV was evaluated using dice similarity coefficient (DSC), Hausdorff distance (HD) and relative volume. Location of largest deviations was evaluated with centroid values. Observer confidence pre- and post-workshop was also recorded using a 6-point Likert scale. The workshop was deemed beneficial for an OAR if ≥50% of observers mean scores improved in any metric and ≥50% of observers' confidence improved. All OARs had ≥50% of observers improve in at least one metric. Base of tongue, larynx, spinal cord and right temporal lobe were the only OARs achieving a mean DSC score of ≥0.7. Base of tongue, left and right lacrimal glands, larynx, left optic nerve and right parotid gland all exhibited statistically significant HD improvements post-workshop (P < 0.05). Brainstem and left and right temporal lobes all had statistically significant relative volume improvements post-workshop (P < 0.05). Post-workshop observer confidence improvement was observed for all OARs (P < 0.001). The educational workshop reduced IOV and improved observers' confidence when delineating nasopharyngeal OARs on MRI.

Testing Segmentation Popular Loss and Variations in Three Multiclass Medical Imaging Problems

Image structures are segmented automatically using deep learning (DL) for analysis and processing. The three most popular base loss functions are cross entropy (crossE), intersect-over-the-union (IoU), and dice. Which should be used, is it useful to consider simple variations, such as modifying formula coefficients? How do characteristics of different image structures influence scores? Taking three different medical image segmentation problems (segmentation of organs in magnetic resonance images (MRI), liver in computer tomography images (CT) and diabetic retinopathy lesions in eye fundus images (EFI)), we quantify loss functions and variations, as well as segmentation scores of different targets. We first describe the limitations of metrics, since loss is a metric, then we describe and test alternatives. Experimentally, we observed that DeeplabV3 outperforms UNet and fully convolutional network (FCN) in all datasets. Dice scored 1 to 6 percentage points (pp) higher than cross entropy over all datasets, IoU improved 0 to 3 pp. Varying formula coefficients improved scores, but the best choices depend on the dataset: compared to crossE, different false positive vs. false negative weights improved MRI by 12 pp, and assigning zero weight to background improved EFI by 6 pp. Multiclass segmentation scored higher than n-uniclass segmentation in MRI by 8 pp. EFI lesions score low compared to more constant structures (e.g., optic disk or even organs), but loss modifications improve those scores significantly 6 to 9 pp. Our conclusions are that dice is best, it is worth assigning 0 weight to class background and to test different weights on false positives and false negatives.

A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the ε-insensitive loss function, the structural risk term, and the core-set principle. We apply this algorithm to the identification of specific target areas in MR images. (2) For each abdominal MRI case, we use four MR sequences (fat, water, in-phase (IP), and opposed-phase (OP)) and the position coordinates (x, y) of each pixel as the input of the algorithm. We use three classifiers to identify the liver and kidneys in the MR images. Experiments show that the proposed method achieves a higher precision in the recognition of specific regions of medical images and has better adaptability in the case of large-scale datasets than the traditional RBF algorithm.