Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis

Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient’s lumbar spine. Our goal is to develop a novel strategy for assessing the extent of Lumbar Spinal Stenosis by automatically calculating these distances from the patient’s lumbar spine MRI. Our method starts with a semantic segmentation of T1- and T2-weighted composite axial MRI images using SegNet that partitions the image into six regions of interest. They consist of three main regions-of-interest, namely the Intervertebral Disc, Posterior Element, and Thecal Sac, and three auxiliary regions-of-interest that includes the Area between Anterior and Posterior elements. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. Nine anatomical landmarks on the image are located by delineating the region boundaries found in the segmented image before the anteroposterior diameter and foraminal widths can be measured. The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine MRI dataset containing MRI studies of 515 patients. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. We found that our method works best when our contour evolution algorithm is applied to improve the accuracy of both the label images used to train the SegNet model and the automatically segmented image. The average error of the calculated right and left foraminal distances relative to their expert-measured distances are 0.28 mm (p = 0.92) and 0.29 mm (p = 0.97), respectively. The average error of the calculated anteroposterior diameter relative to their expert-measured diameter is 0.90 mm (p = 0.92). The method also achieves 96.7% agreement with an expert opinion on determining the severity of the Intervertebral Disc herniations

Development of Ground Truth Data for Automatic Lumbar Spine MRI Image Segmentation

Artificial Intelligence through supervised machine learning remains an attractive and popular research area in medical image processing. The objective of such research is often tied to the development of an intelligent computer aided diagnostic system whose aim is to assist physicians in their task of diagnosing diseases. The quality of the resulting system depends largely on the availability of good data for the machine learning algorithm to train on. Training data of a supervised learning process needs to include ground truth, i.e., data that have been correctly annotated by experts. Due to the complex nature of most medical images, human error, experience, and perception play a strong role in the quality of the ground truth. In this paper, we present the results of annotating lumbar spine Magnetic Resonance Imaging images for automatic image segmentation and propose confidence and consistency metrics to measure the quality and variability of the resulting ground truth data, respectively

Segmentation of Lumbar Spine MRI Images for Stenosis Detection using Patch-based Pixel Classification Neural Network

This paper addresses the central problem of automatic segmentation of lumbar spine Magnetic Resonance Imaging (MRI) images to delineate boundaries between the anterior arch and posterior arch of the lumbar spine. This is necessary to efficiently detect the occurrence of lumbar spinal stenosis as a leading cause of Chronic Lower Back Pain. A patch-based classification neural network consisting of convolutional and fully connected layers is used to classify and label pixels in MRI images. The classifier is trained using overlapping patches of size 25x25 pixels taken from a set of cropped axial-view T2-weighted MRI images of the bottom three intervertebral discs. A set of experiment is conducted to measure the performance of the classification network in segmenting the images when either all or each of the discs separately is used. Using pixel accuracy, mean accuracy, mean Intersection over Union (IoU), and frequency weighted IoU as the performance metrics we have shown that our approach produces better segmentation results than eleven other pixel classifiers. Furthermore, our experiment result also indicates that our approach produces more accurate delineation of all important boundaries and making it best suited for the subsequent stage of lumbar spinal stenosis detection

Ataxin-3 Plays a Role in Mouse Myogenic Differentiation through Regulation of Integrin Subunit Levels

BACKGROUND: During myogenesis several transcription factors and regulators of protein synthesis and assembly are rapidly degraded by the ubiquitin-proteasome system (UPS). Given the potential role of the deubiquitinating enzyme (DUB) ataxin-3 in the UPS, and the high expression of the murine ataxin-3 homolog in muscle during embryogenesis, we sought to define its role in muscle differentiation. METHODOLOGY/PRINCIPAL FINDINGS: Using immunofluorescence analysis, we found murine ataxin-3 (mATX3) to be highly expressed in the differentiated myotome of E9.5 mouse embryos. C2C12 myoblasts depleted of mATX3 by RNA interference exhibited a round morphology, cell misalignment, and a delay in differentiation following myogenesis induction. Interestingly, these cells showed a down-regulation of alpha5 and alpha7 integrin subunit levels both by immunoblotting and immunofluorescence. Mouse ATX3 was found to interact with alpha5 integrin subunit and to stabilize this protein by repressing its degradation through the UPS. Proteomic analysis of mATX3-depleted C2C12 cells revealed alteration of the levels of several proteins related to integrin signaling. CONCLUSIONS: Ataxin-3 is important for myogenesis through regulation of integrin subunit levels.This work was financed by the Fundacao para a Ciencia e a Tecnologia (FCT) (POCI/SAU-MMO/60412/2002) and by National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH/NINDS) grant RO1 NS038712 to HLP. MCC, FB, AJR, and RJT were supported by the FCT fellowships (SFRH/BD/9759/2003 and SFRH/BPD/28560/2006), (SFRH/BPD/17368/2004), (SFRH/BD/17066/2004), (SFRH/BD/29947/2006), respectively. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Genetic redundancies enhance information transfer in noisy regulatory circuits

[EN] Cellular decision making is based on regulatory circuits that associate signal thresholds to specific physiological actions. This transmission of information is subjected to molecular noise what can decrease its fidelity. Here, we show instead how such intrinsic noise enhances information transfer in the presence of multiple circuit copies. The result is due to the contribution of noise to the generation of autonomous responses by each copy, which are altogether associated with a common decision. Moreover, factors that correlate the responses of the redundant units (extrinsic noise or regulatory cross-talk) contribute to reduce fidelity, while those that further uncouple them (heterogeneity within the copies) can lead to stronger information gain. Overall, our study emphasizes how the interplay of signal thresholding, redundancy, and noise influences the accuracy of cellular decision making. Understanding this interplay provides a basis to explain collective cell signaling mechanisms, and to engineer robust decisions with noisy genetic circuits.This work has been supported by BFU2015-66894-P (MINECO/FEDER) and GV/2016/079 (GVA) Grants. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Rodrigo Tarrega, G.; Poyatos, JF. (2016). Genetic redundancies enhance information transfer in noisy regulatory circuits. PLoS Computational Biology. 12(10). https://doi.org/10.1371/journal.pcbi.1005156S121

Boundary Delineation of MRI Images for Lumbar Spinal Stenosis Detection through Semantic Segmentation using Deep Neural Networks

We propose a methodology to aid clinicians in performing lumbar spinal stenosis detection through semantic segmentation and delineation of Magnetic Resonance Imaging (MRI) scans of lumbar spine using deep learning. Our dataset contains MRI studies of 515 patients with symptomatic back pains. Each study is annotated by expert radiologists with notes regarding the observed characteristics and condition of the lumbar spine. We have developed a ground truth dataset, containing image labels of four important regions in the lumbar spine, to be used as training and test images to develop classification models for segmentation. We developed two novel metrics, namely confidence and consistency, to assess the quality of the ground truth dataset through a derivation of the Jaccard Index. We experimented with semantic segmentation of our dataset using SegNet. Our evaluation of the segmentation and the delineation results show that our proposed methodology produces very good performance as measured by several contour-based and region-based metrics. Additionally, using the Cohen’s kappa and frequency-weighted confidence metrics, we can show that 1) the model’s performance is within the range of the worst and the best manual labelling results, and 2) the ground-truth dataset has an excellent inter-rater agreement score. We also presented two representative delineation results of the worst and best segmentation based on their BF-score to show visually how accurate and suitable the results are for computer-aided-diagnosis purposes

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