The multi-level classification network (MCN) with modified residual U-Net for ischemic stroke lesions segmentation from ATLAS.

Ischemic and hemorrhagic strokes are two major types of internal brain injury. 3D brain MRI is suggested by neurologists to examine the brain. Manual examination of brain MRI is very sensitive and time-consuming task. However, automatic diagnosis can assist doctors in this regard. Anatomical Tracings of Lesions After Stroke (ATLAS) is publicly available dataset for research experiments. One of the major issues in medical imaging is class imbalance. Similarly, pixel representation of stroke lesion is less than 1% in ATLAS. Second major challenge in this dataset is inter-class similarity. A multi-level classification network (MCN) is proposed for segmentation of ischemic stroke lesions. MCN consists of three cascaded discrete networks. The first network designed to reduce the slice level class imbalance, where a classifier model is trained to extract the slices of stroke lesions from a whole brain MRI volume. The interclass similarity cause to produce false positives in segmented output. Therefore, all extracted stroke slices were divided into overlapping patches (64 × 64) and carried to the second network. The task associated with second network is to classify the patches comprises of stroke lesion. The third network is a 2D modified residual U-Net that segments out the stroke lesions from the patches extracted by the second network. MCN achieved 0.754 mean dice score on test dataset which is higher than the other state-of-the-art methods on the same dataset.

DenseRes-Unet: Segmentation of overlapped/clustered nuclei from multi organ histopathology images.

Cancer is the second deadliest disease globally that can affect any human body organ. Early detection of cancer can increase the chances of survival in humans. Morphometric appearances of histopathology images make it difficult to segment nuclei effectively. We proposed a model to segment overlapped nuclei from H&E stained images. U-Net model achieved state-of-the-art performance in many medical image segmentation tasks; however, we modified the U-Net to learn a distinct set of consistent features. In this paper, we proposed the DenseRes-Unet model by integrating dense blocks in the last layers of the encoder block of U-Net, focused on relevant features from previous layers of the model. Moreover, we take advantage of residual connections with Atrous blocks instead of conventional skip connections, which helps to reduce the semantic gap between encoder and decoder paths. The distance map and binary threshold techniques intensify the nuclei interior and contour information in the images, respectively. The distance map is used to detect the center point of nuclei; moreover, it differentiates among nuclei interior boundary and core area. The distance map lacks a contour problem, which is resolved by using a binary threshold. Binary threshold helps to enhance the pixels around nuclei. Afterward, we fed images into the proposed DenseRes-Unet model, a deep, fully convolutional network to segment nuclei in the images. We have evaluated our model on four publicly available datasets for Nuclei segmentation to validate the model's performance. Our proposed model achieves 89.77% accuracy 90.36% F1-score, and 78.61% Aggregated Jaccard Index (AJI) on Multi organ Nucleus Segmentation (MoNuSeg).

Multi-level Kronecker Convolutional Neural Network (ML-KCNN) for Glioma Segmentation from Multi-modal MRI Volumetric Data.

The development of an automated glioma segmentation system from MRI volumes is a difficult task because of data imbalance problem. The ability of deep learning models to incorporate different layers for data representation assists medical experts like radiologists to recognize the condition of the patient and further make medical practices easier and automatic. State-of-the-art deep learning algorithms enable advancement in the medical image segmentation area, such a segmenting the volumes into sub-tumor classes. For this task, fully convolutional network (FCN)-based architectures are used to build end-to-end segmentation solutions. In this paper, we proposed a multi-level Kronecker convolutional neural network (MLKCNN) that captures information at different levels to have both local and global level contextual information. Our ML-KCNN uses Kronecker convolution, which overcomes the missing pixels problem by dilated convolution. Moreover, we used a post-processing technique to minimize false positive from segmented outputs, and the generalized dice loss (GDL) function handles the data-imbalance problem. Furthermore, the combination of connected component analysis (CCA) with conditional random fields (CRF) used as a post-processing technique achieves reduced Hausdorff distance (HD) score of 3.76 on enhancing tumor (ET), 4.88 on whole tumor (WT), and 5.85 on tumor core (TC). Dice similarity coefficient (DSC) of 0.74 on ET, 0.90 on WT, and 0.83 on TC. Qualitative and visual evaluation of our proposed method shown effectiveness of the proposed segmentation method can achieve performance that can compete with other brain tumor segmentation techniques.

Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities.

Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing information of users, tasks, access policies and resources in such applications. It also does not incorporate semantically meaningful business roles, which could have a diverse impact upon access decisions in such multi-domain collaborative business environments. We propose an Intelligent Role-based Access Control (I-RBAC) model that uses intelligent software agents for achieving intelligent access control in such highly dynamic multi-domain environments. The novelty of this model lies in using a core I-RBAC ontology that is developed using real-world semantic business roles as occupational roles provided by Standard Occupational Classification (SOC), USA. It contains around 1400 business roles, from nearly all domains, along with their detailed task descriptions as well as hierarchical relationships among them. The semantic role mining process is performed through intelligent agents that use word embedding and a bidirectional LSTM deep neural network for automated population of organizational ontology from its unstructured text policy and, subsequently, matching this ontology with core I-RBAC ontology to extract unified business roles. The experimentation was performed on a large number of collaboration case scenarios of five multi-domain organizations and promising results were obtained regarding the accuracy of automatically derived RDF triples (Subject, Predicate, Object) from organizational text policies as well as the accuracy of extracted semantically meaningful roles.

Visual interpretability in 3D brain tumor segmentation network.

Medical image segmentation is a complex yet one of the most essential tasks for diagnostic procedures such as brain tumor detection. Several 3D Convolutional Neural Network (CNN) architectures have achieved remarkable results in brain tumor segmentation. However, due to the black-box nature of CNNs, the integration of such models to make decisions about diagnosis and treatment is high-risk in the domain of healthcare. It is difficult to explain the rationale behind the model's predictions due to the lack of interpretability. Hence, the successful deployment of deep learning models in the medical domain requires accurate as well as transparent predictions. In this paper, we generate 3D visual explanations to analyze the 3D brain tumor segmentation model by extending a post-hoc interpretability technique. We explore the advantages of a gradient-free interpretability approach over gradient-based approaches. Moreover, we interpret the behavior of the segmentation model with respect to the input Magnetic Resonance Imaging (MRI) images and investigate the prediction strategy of the model. We also evaluate the interpretability methodology quantitatively for medical image segmentation tasks. To deduce that our visual explanations do not represent false information, we validate the extended methodology quantitatively. We learn that the information captured by the model is coherent with the domain knowledge of human experts, making it more trustworthy. We use the BraTS-2018 dataset to train the 3D brain tumor segmentation network and perform interpretability experiments to generate visual explanations.

Extraction of region of interest from brain MRI by converting images into neutrosophic domain using the modified S-function.

Purpose: A brain tumor is deadly as its exact extraction is tricky. However, at times, its removal is the only way to save a patient, leaving very little room for the doctors to make a mistake. Image segmentation algorithms can be used to detect tumor in magnetic resonance imaging (MRI). Irregularity in size, location, and shape of tumor in brain with imbalanced distribution of classes in the dataset make this a challenging task. To deal with these challenges, a region of interest (ROI) is extracted from images by removing redundant information. Approach: We present a process to extract ROIs by converting images into neutrosophic domain. Two modalities FLAIR and T2 were diffused to reduce inhomogeneity in nontumorous regions and then anisotropic diffusion is applied to reduce the noise. The ROIs, which are tumorous regions, were extracted using neutrosophic technique based on the modified S-function. The extracted ROIs were refined by applying the morphological operations in the end. Results: We evaluated our proposed method using three datasets including BraTS 2019 and compared the results with state-of-the-art methods. The parameters sensitivity, false negative rate, and ratio of ROI area to slice area were calculated to evaluate the proposed method. These parameters indicate that the proposed method achieved more than 98% sensitivity, 1.5% false negative rate, and removed more than 80% redundancy. Conclusions: Evaluating parameters indicate that the method proposed has removed most of the redundant data from MRI images and extracted ROIs are composed of tumorous region.

State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms.

Several neuroimaging processing applications consider skull stripping as a crucial pre-processing step. Due to complex anatomical brain structure and intensity variations in brain magnetic resonance imaging (MRI), an appropriate skull stripping is an important part. The process of skull stripping basically deals with the removal of the skull region for clinical analysis in brain segmentation tasks, and its accuracy and efficiency are quite crucial for diagnostic purposes. It requires more accurate and detailed methods for differentiating brain regions and the skull regions and is considered as a challenging task. This paper is focused on the transition of the conventional to the machine- and deep-learning-based automated skull stripping methods for brain MRI images. It is observed in this study that deep learning approaches have outperformed conventional and machine learning techniques in many ways, but they have their limitations. It also includes the comparative analysis of the current state-of-the-art skull stripping methods, a critical discussion of some challenges, model of quantifying parameters, and future work directions.

A Multiobjective, Lion Mating Optimization Inspired Routing Protocol for Wireless Body Area Sensor Network Based Healthcare Applications.

The importance of body area sensor networks (BASNs) is increasing day by day because of their increasing use in Internet of things (IoT)-enabled healthcare application services. They help humans in improving their quality of life by continuously monitoring various vital signs through biosensors strategically placed on the human body. However, BASNs face serious challenges, in terms of the short life span of their batteries and unreliable data transmission, because of the highly unstable and unpredictable channel conditions of tiny biosensors located on the human body. These factors may result in poor data gathering quality in BASNs. Therefore, a more reliable data transmission mechanism is greatly needed in order to gather quality data in BASN-based healthcare applications. Therefore, this study proposes a novel, multiobjective, lion mating optimization inspired routing protocol, called self-organizing multiobjective routing protocol (SARP), for BASN-based IoT healthcare applications. The proposed routing scheme significantly reduces local search problems and finds the best dynamic cluster-based routing solutions between the source and destination in BASNs. Thus, it significantly improves the overall packet delivery rate, residual energy, and throughput with reduced latency and packet error rates in BASNs. Extensive simulation results validate the performance of our proposed SARP scheme against the existing routing protocols in terms of the packet delivery ratio, latency, packet error rate, throughput, and energy efficiency for BASN-based health monitoring applications.

QoSRP: A Cross-layer QoS Channel-Aware Routing Protocol for the Internet of Underwater Acoustic Sensor Networks.

Quality of service (QoS)-aware data gathering in static-channel based underwater wireless sensor networks (UWSNs) is severely limited due to location and time-dependent acoustic channel communication characteristics. This paper proposes a novel cross-layer QoS-aware multichannel routing protocol called QoSRP for the internet of UWSNs-based time-critical marine monitoring applications. The proposed QoSRP scheme considers the unique characteristics of the acoustic communication in highly dynamic network topology during gathering and relaying events data towards the sink. The proposed QoSRP scheme during the time-critical events data-gathering process employs three basic mechanisms, namely underwater channel detection (UWCD), underwater channel assignment (UWCA) and underwater packets forwarding (UWPF). The UWCD mechanism finds the vacant channels with a high probability of detection and low probability of missed detection and false alarms. The UWCA scheme assigns high data rates channels to acoustic sensor nodes (ASNs) with longer idle probability in a robust manner. Lastly, the UWPF mechanism during conveying information avoids congestion, data path loops and balances the data traffic load in UWSNs. The QoSRP scheme is validated through extensive simulations conducted by NS2 and AquaSim 2.0 in underwater environments (UWEs). The simulation results reveal that the QoSRP protocol performs better compared to existing routing schemes in UWSNs.