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

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

© Springer International Publishing AG 2017. 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

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), it cost the United Kingdom (UK) government over £1.3 million per day. In fact a very high proportion of the UK population will complain from their back pain. Fur-thermore, Magnetic Resonance Imaging (MRI) is one of the main diagnosing procedure for LBP. Automatic disc labeling in the MRI to detect the herniation area will reduce the required time to issue the report from the radiologist. We present a method for automatic labeling for the lumbar spine disc area using the axial view MRI based on the pixels coordinate and gray level features. We use a clinical MRI for the training and testing. Moreover, the accuracy and the recon-structed images was the main indicator for our result. The highest achieved ac-curacy was 98.9 and 91.1 for Weighted KNN and Fine Gaussian SVM respec-tively

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

قواعد نفي الحرج وأثرها زمن وباء كورونا: دراسة تأصيلية مقاصدية فقهية

الأهداف: يهدف هذا البحث لبيان أثر قواعد التيسير، ورفع الحرج في الأحكام الفقهية زمن فيروس كورونا. المنهجية: اتبع الباحث في ذلك المنهج الاستقرائي باستقراء أقوال المذاهب والفقهاء في كل مسألة، والمنهج المقارن، بمقارنة أقوالهم وأدلتهم والترجيح بينها. النتائج: خلص هذا البحث إلى أن لقواعد الشريعة الفقهية الكلية المتضمنة للتيسير ورفع الحرج، أثرا عظيما في إصدار الأحكام الفقهية زمن المستجدات والنوازل المعاصرة، ومنها فيروس كورونا، حيث كان لها مدخل عظيم في تسهيل كثير من الأحكام الفقهية على الناس، ورفع المشقة عنهم، وهي مع ذلك لم تخرج تلك الأحكام بذلك عن نطاق الحق وموافقة الشرع، إذ الشرع أصلا جاء لجلب مصالح العباد، ودفع المفاسد عنهم، وظهر ذلك جليا في التخفيف في طهارة مريض كورونا وصلاته، وتيسير أمور عبادات الناس ومعاملاتهم. التوصيات: يوصي البحث بالتعريف بمنهج الشريعة الإسلامية في التيسير، ودفع الحرج من خلال المحاضرات والمؤتمرات الدولية، والتعريف بالأحكام الفقهية أثناء النوازل، مستصحبين روح الشريعة الإسلامية في التخفيف والتيسير ، مما يؤكد كونها شريعة ربانية صالحة لكل زمان ومكان وظرف

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

© Springer International Publishing AG 2017. 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