A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI

We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies and convolutional image-to-image translation followed by beam search to label vertebral levels in a self-consistent manner. The method can be applied without modification to lumbar, cervical and thoracic-only scans across a range of different MR sequences. The resulting system achieves 98.1% detection rate and 96.5% identification rate on a challenging clinical dataset of whole spine scans and matches or exceeds the performance of previous systems on lumbar-only scans. Finally, we demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.Comment: Accepted full paper to Medical Image Computing and Computer Assisted Intervention 2020. 11 pages plus appendi

Weakly-Supervised Evidence Pinpointing and Description

We propose a learning method to identify which specific regions and features of images contribute to a certain classification. In the medical imaging context, they can be the evidence regions where the abnormalities are most likely to appear, and the discriminative features of these regions supporting the pathology classification. The learning is weakly-supervised requiring only the pathological labels and no other prior knowledge. The method can also be applied to learn the salient description of an anatomy discriminative from its background, in order to localise the anatomy before a classification step. We formulate evidence pinpointing as a sparse descriptor learning problem. Because of the large computational complexity, the objective function is composed in a stochastic way and is optimised by the Regularised Dual Averaging algorithm. We demonstrate that the learnt feature descriptors contain more specific and better discriminative information than hand-crafted descriptors contributing to superior performance for the tasks of anatomy localisation and pathology classification respectively. We apply our method on the problem of lumbar spinal stenosis for localising and classifying vertebrae in MRI images. Experimental results show that our method when trained with only target labels achieves better or competitive performance on both tasks compared with strongly-supervised methods requiring labels and multiple landmarks. A further improvement is achieved with training on additional weakly annotated data, which gives robust localisation with average error within 2 mm and classification accuracies close to human performance

Radiological Grading of Spinal MRI

This paper describes a fully automatic system for obtaining the standard Pfi rrmann degeneration grading of individual intervertebral spinal discs in T2 MRI scans. It involves detecting and labelling all the vertebrae in the scan and then learning a regression from the disc region to the grading. In developing the regression function we investigate a spectrum of support regions which involve di ering degrees of segmentation of the scan: our intention is to ascertain to what extent segmentation is necessary or detrimental in obtaining robust and accurate measurements. The methods are assessed on a heterogeneous clinical dataset containing 1710 P rrmann-graded discs, from 285 symptomatic back pain patients. We are able to predict the grade to 1 precision at 85.8% accuracy. Our novel method proposes new image features that outperform previous features and utilizes techniques to improve robustness to MR imaging variations

Radiological Grading of Spinal MRI

This paper describes a fully automatic system for obtaining the standard Pfi rrmann degeneration grading of individual intervertebral spinal discs in T2 MRI scans. It involves detecting and labelling all the vertebrae in the scan and then learning a regression from the disc region to the grading. In developing the regression function we investigate a spectrum of support regions which involve di ering degrees of segmentation of the scan: our intention is to ascertain to what extent segmentation is necessary or detrimental in obtaining robust and accurate measurements. The methods are assessed on a heterogeneous clinical dataset containing 1710 P rrmann-graded discs, from 285 symptomatic back pain patients. We are able to predict the grade to 1 precision at 85.8% accuracy. Our novel method proposes new image features that outperform previous features and utilizes techniques to improve robustness to MR imaging variations

Vertebrae detection and labelling in lumbar MR images

We describe a method to automatically detect and label the vertebrae in human lumbar spine MRI scans. Our contribution is to show that marrying two strong algorithms (the DPM object detector of Felzenszwalb et al. [1], and inference using dynamic programming on chains) together with appropriate modelling, results in a simple, computationally cheap procedure, that achieves state-of-the-art performance. The training of the algorithm is principled, and heuristics are not required. The method is evaluated quantitatively on a dataset of 371 MRI scans, and it is shown that the method copes with pathologies such as scoliosis, joined vertebrae, deformed vertebrae and disks, and imaging artifacts. We also demonstrate that the same method is applicable (without retraining) to CT scans

ISSLS Prize in Bioengineering Science 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist.

Study design Investigation of the automation of radiological features from magnetic resonance images (MRIs) of the lumbar spine. Objective To automate the process of grading lumbar intervertebral discs and vertebral bodies from MRIs. Summary of background data MR imaging is the most common imaging technique used in investigating low back pain (LBP). Various features of degradation, based on MRIs, are commonly recorded and graded, e.g., Modic change and Pfirrmann grading of intervertebral discs. Consistent scoring and grading is important for developing robust clinical systems and research. Automation facilitates this consistency and reduces the time of radiological analysis considerably and hence the expense. Methods 12,018 intervertebral discs, from 2009 patients, were graded by a radiologist and were then used to train: (1) a system to detect and label vertebrae and discs in a given scan, and (2) a convolutional neural network (CNN) model that predicts several radiological gradings. The performance of the model, in terms of class average accuracy, was compared with the intra-observer class average accuracy of the radiologist. Results The detection system achieved 95.6% accuracy in terms of disc detection and labeling. The model is able to produce predictions of multiple pathological gradings that consistently matched those of the radiologist. The model identifies ‘Evidence Hotspots’ that are the voxels that most contribute to the degradation scores. Conclusions Automation of radiological grading is now on par with human performance. The system can be beneficial in aiding clinical diagnoses in terms of objectivity of gradings and the speed of analysis. It can also draw the attention of a radiologist to regions of degradation. This objectivity and speed is an important stepping stone in the investigation of the relationship between MRIs and clinical diagnoses of back pain in large cohorts.</p

Lumbar spine discs labeling using axial view MRI based on the pixels coordinate and gray level features

Disc herniation is a major reason for lower back pain (LBP), a health issue that affects a very high proportion of the UK population and is costing the UK government over £1.3 million per day in health care cost. Currently, the process to diagnose the cause of LBP involves examining a large number of Magnetic Resonance Images (MRI) but this process is both expensive in terms time and effort. Automatic labeling of lumbar disc pixels in the MRI to detect the herniation area will reduce the time to diagnose and detect the cause of LBP by the physicians. In this paper, we present a method for automatic labeling of the lumbar spine disc pixels in axial view MRI using pixels locations and gray level as features. Clinical MRIs are used for the training and testing of the method. The pixel classification accuracy and the quality of the reconstructed disc images are used as the main performance indicators for our method. Our experiments show that high level of classification accuracy of 91.1% and 98.9% can be achieved using Weighted KNN and Fine Gaussian SVM classifiers respectively