MRI image segmentation using machine learning networks and level set approaches

<span>The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones.</span>

Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN

Since the past decade, the deep learning techniques are widely used in research. The objective of various applications is achieved using these techniques. The deep learning technique in the medical field helps to find medicines and diagnosis of diseases. The Alzheimer’s is a physical brain disease, on which recently many research are experimented to develop an efficient model that diagnoses the early stages of Alzheimer’s disease. In this paper, a Hybrid model is proposed, which is a combination of VGG19 with additional layers, and a CNN deep learning model for detecting and classifying the different stages of Alzheimer’s and the performance is compared with the CNN model. The Magnetic Resonance Images are used to analyse both models received from the Kaggle dataset. The result shows that the Hybrid model works efficiently in detecting and classifying the different stages of Alzheimer’s.

Factorisation-Based Image Labelling

Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.

Odontogenic-Like Pain in Partial Edentulism: An Unusual Presentation of Diffuse Large B-Cell Lymphoma of the Mandible

Primary bone lymphoma is a rare entity and it usually occurs in long bones. Primary mandibular involvement is very rare, and it usually shows unspecific features, mimicking odontogenic inflammatory lesions. We present the unusual case of a diffuse large B-cell lymphoma (DLBCL) of the right mandibular body in a 91-year-old woman, who presented with acute pain in the mandibular region initially suspicious for odontogenic abscess. No significant findings were seen on orthopantomography (OPG) and her almost complete edentulism made the diagnosis of abscess unlikely. Computed tomography and magnetic resonance images showed an expansive mass around the right mandibular body with erosion of cortical bone and involving the right mandibular canal and nerve. Final diagnosis of DLBCL was pathologically proven. The presence of odontogenic-like pain in nearly complete edentulism should be suspicious for malignancy, and it needs further diagnostic workup despite the absence of signs on OPG.

The prevalence and distribution of the flexor carpi radialis brevis muscle in the Turkish population

The flexor carpi radialis brevis (FCRB) is a very rare anomalous muscle that is usually asymptomatic but may cause various pathologies, such as radial-sided wrist pain. The aim of this study was to determine the prevalence of FCRB in the Turkish population, its location, and sex differences. Forearm, wrist, and hand magnetic resonance images of 849 individuals aged 18–65 years were retrospectively evaluated in this study. The survey found an FCRB prevalence of 4%, with a prevalence of 3.6% among women and of 4.7% among men. However, the difference between the sexes was not statistically significant (p = 0.629). The origin of all 34 FCRBs identified was the distal third of the anterior aspect of the radius; the insertion site of 28 was the second metacarpal bone, whereas that of the remaining 6 was the os trapezium. In conclusion, the data of this study report the prevalence of FCRB for the first time in the Turkish population, which will contribute to radiological and surgical evaluations in the region and help in early and accurate diagnosis of various pathological conditions that may be caused by FCRB.

Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning

Temporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This system uses deep learning-based semantic segmentation approaches. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional neural network encoder-decoder named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice for the assessment of temporomandibular disorders.

Brain Tumour Classification Using Noble Deep Learning Approach with Parametric Optimization through Metaheuristics Approaches

Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a unique CNN architecture which varies from those usually used in computer vision. The classification of tumour cells is very difficult due to their heterogeneous nature. From a visual learning and brain tumour recognition point of view, a convolutional neural network (CNN) is the most extensively used machine learning algorithm. This paper presents a CNN model along with parametric optimization approaches for analysing brain tumour magnetic resonance images. The accuracy percentage in the simulation of the above-mentioned model is exactly 100% throughout the nine runs, i.e., Taguchi’s L9 design of experiment. This comparative analysis of all three algorithms will pique the interest of readers who are interested in applying these techniques to a variety of technical and medical challenges. In this work, the authors have tuned the parameters of the convolutional neural network approach, which is applied to the dataset of Brain MRIs to detect any portion of a tumour, through new advanced optimization techniques, i.e., SFOA, FBIA and MGA.

A vaginal fibroepithelial stromal polyp: a case report with magnetic resonance images

A fibroepithelial stromal polyp is a benign soft tissue tumour that can occur in the vagina, vulva and uterine cervix. Magnetic resonance imaging (MRI) findings have been reported in patients with vulvar fibroepithelial stromal polyps, not in those with vaginal polyps. We present MRI findings of vaginal fibroepithelial stromal polyp in a postmenopausal female. A 1 to 2 cm firm vaginal mass arising from the left side of the vaginal wall with hypointense signal changes on T1W MRI was identified. A well-defined vaginal mass (1 cm diameter) was detected with inhomogeneous signal intensity on T2W images. However, a major portion had high signal intensity on diffusion-weighted images. A benign vaginal lesion with oedematous changes or myxoid degeneration was suspected. Vaginal resection was performed, and fibroepithelial stromal polyp was pathologically diagnosed. MRI may be a useful non-invasive modality for preoperatively diagnosing vaginal fibroepithelial stromal polyps.