Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring.

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and postoperative follow-up. Automated segmentation of cerebrovascular anatomy from Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) can provide radiologists with a more detailed and precise view of these vessels. This paper introduces a domain generalized artificial intelligence (AI) solution for volumetric monitoring of cerebrovascular structures from multi-center MRAs. Our approach utilizes a multi-task deep convolutional neural network (CNN) with a topology-aware loss function to learn voxel-wise segmentation of the cerebrovascular tree. We use Decorrelation Loss to achieve domain regularization for the encoder network and auxiliary tasks to provide additional regularization and enable the encoder to learn higher-level intermediate representations for improved performance. We compare our method to six state-of-the-art 3D vessel segmentation methods using retrospective TOF-MRA datasets from multiple private and public data sources scanned at six hospitals, with and without vascular pathologies. The proposed model achieved the best scores in all the qualitative performance measures. Furthermore, we have developed an AI-assisted Graphical User Interface (GUI) based on our research to assist radiologists in their daily work and establish a more efficient work process that saves time.

Multi-Level Residual Dual Attention Network for Major Cerebral Arteries Segmentation in MRA Toward Diagnosis of Cerebrovascular Disorders.

Segmentation of major brain vessels is very important for the diagnosis of cerebrovascular disorders and subsequent surgical planning. Vessel segmentation is an important preprocessing step for a wide range of algorithms for the automatic diagnosis or treatment of several vascular pathologies and as such, it is valuable to have a well-performing vascular segmentation pipeline. In this article, we propose an end-to-end multiscale residual dual attention deep neural network for resilient major brain vessel segmentation. In the proposed network, the encoder and decoder blocks of the U-Net are replaced with the multi-level atrous residual blocks to enhance the learning capability by increasing the receptive field to extract the various semantic coarse- and fine-grained features. Dual attention block is incorporated in the bottleneck to perform effective multiscale information fusion to obtain detailed structure of blood vessels. The methods were evaluated on the publicly available TubeTK data set. The proposed method outperforms the state-of-the-art techniques with dice of 0.79 on the whole-brain prediction. The statistical and visual assessments indicate that proposed network is robust to outliers and maintains higher consistency in vessel continuity than the traditional U-Net and its variations.

Feasibility of Coacervate-Like Nanostructure for Instant Drug Nanoformulation.

Despite the enormous advancements in nanomedicine research, a limited number of nanoformulations are available on the market, and few have been translated to clinics. An easily scalable, sustainable, and cost-effective manufacturing strategy and long-term stability for storage are crucial for successful translation. Here, we report a system and method to instantly formulate NF achieved with a nanoscale polyelectrolyte coacervate-like system, consisting of anionic pseudopeptide poly(l-lysine isophthalamide) derivatives, polyethylenimine, and doxorubicin (Dox) via simple "mix-and-go" addition of precursor solutions in seconds. The coacervate-like nanosystem shows enhanced intracellular delivery of Dox to patient-derived multidrug-resistant (MDR) cells in 3D tumor spheroids. The results demonstrate the feasibility of an instant drug formulation using a coacervate-like nanosystem. We envisage that this technique can be widely utilized in the nanomedicine field to bypass the special requirement of large-scale production and elongated shelf life of nanomaterials.

Nested U-Net for Segmentation of Red Lesions in Retinal Fundus Images and Sub-image Classification for Removal of False Positives.

Diabetic retinopathy is a pathological change of the retina that occurs for long-term diabetes. The patients become symptomatic in advanced stages of diabetic retinopathy resulting in severe non-proliferative diabetic retinopathy or proliferative diabetic retinopathy stages. There is a need of an automated screening tool for the early detection and treatment of patients with diabetic retinopathy. This paper focuses on the segmentation of red lesions using nested U-Net Zhou et al. (Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, 2018) followed by removal of false positives based on the sub-image classification method. Different sizes of sub-images were studied for the reduction in false positives in the sub-image classification method. The network could capture semantic features and fine details due to dense convolutional blocks connected via skip connections in between down sampling and up sampling paths. False-negative candidates were very few and the sub-image classification network effectively reduced the falsely detected candidates. The proposed framework achieves a sensitivity of [Formula: see text], precision of [Formula: see text], and F1-Score of [Formula: see text] for the DIARETDB1 data set Kalviainen and Uusutalo (Medical Image Understanding and Analysis, Citeseer, 2007). It outperforms the state-of-the-art networks such as U-Net Ronneberger et al. (International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015) and attention U-Net Oktay et al. (Attention u-net: Learning where to look for the pancreas, 2018).

Dissecting multi drug resistance in head and neck cancer cells using multicellular tumor spheroids.

One of the hallmarks of cancers is their ability to develop resistance against therapeutic agents. Therefore, developing effective in vitro strategies to identify drug resistance remains of paramount importance for successful treatment. One of the ways cancer cells achieve drug resistance is through the expression of efflux pumps that actively pump drugs out of the cells. To date, several studies have investigated the potential of using 3-dimensional (3D) multicellular tumor spheroids (MCSs) to assess drug resistance; however, a unified system that uses MCSs to differentiate between multi drug resistance (MDR) and non-MDR cells does not yet exist. In the present report we describe MCSs obtained from post-diagnosed, pre-treated patient-derived (PTPD) cell lines from head and neck squamous cancer cells (HNSCC) that often develop resistance to therapy. We employed an integrated approach combining response to clinical drugs and screening cytotoxicity, monitoring real-time drug uptake, and assessing transporter activity using flow cytometry in the presence and absence of their respective specific inhibitors. The report shows a comparative response to MDR, drug efflux capability and reactive oxygen species (ROS) activity to assess the resistance profile of PTPD MCSs and two-dimensional (2D) monolayer cultures of the same set of cell lines. We show that MCSs provide a robust and reliable in vitro model to evaluate clinical relevance. Our proposed strategy can also be clinically applicable for profiling drug resistance in cancers with unknown resistance profiles, which consequently can indicate benefit from downstream therapy.

Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval.

Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system for lung nodules using optimal feature sets and learning to enhance the performance of retrieval. The classifiers with more features suffer from the curse of dimensionality. Like classification schemes, we found that the optimal feature set selected using the minimal-redundancy-maximal-relevance (mRMR) feature selection technique improves the precision performance of simple distance-based retrieval (SDR). The performance of the classifier is always superior to SDR, which leans researchers towards conventional classifier-based retrieval (CCBR). While CCBR improves the average precision and provides 100% precision for correct classification, it fails for misclassification leading to zero retrieval precision. The class membership-based retrieval (CMR) is found to bridge this gap for texture-based retrieval. Here, CMR is proposed for nodule retrieval using shape-, margin-, and texture-based features. It is found again that optimal feature set is important for the classifier used in CMR as well as for the feature set used for retrieval, which may lead to different feature sets. The proposed system is evaluated using two independent databases from two continents: a public database LIDC/IDRI and a private database PGIMER-IITKGP, using three distance metrics, i.e., Canberra, City block, and Euclidean. The proposed CMR-based retrieval system with optimal feature sets performs better than CCBR and SDR with optimal features in terms of average precision. Apart from average precision and standard deviation of precision, the fraction of queries with zero precision retrieval is also measured.

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy "1","2" as benign and "4","5" as malignant), configuration-2 (composite rank of malignancy "1","2", "3" as benign and "4","5" as malignant), and configuration-3 (composite rank of malignancy "1","2" as benign and "3","4","5" as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.

Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user. Several shape-based, margin-based, and texture-based features are computed to represent the pulmonary nodules. A set of relevant features is determined for the efficient representation of nodules in the feature space. The proposed classification scheme is validated on a data set of 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative public database. The proposed classification scheme is evaluated for three configurations such as configuration 1 (composite rank of malignancy "1" and "2" as benign and "4" and "5" as malignant), configuration 2 (composite rank of malignancy "1","2", and "3" as benign and "4" and "5" as malignant), and configuration 3 (composite rank of malignancy "1" and "2" as benign and "3","4" and "5" as malignant). The performance of the classification is evaluated in terms of area (A z) under the receiver operating characteristic curve. The A z achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 0.9505, 0.8822, and 0.8488, respectively. The proposed method outperforms the most recent technique, which depends on the manual segmentation of pulmonary nodules by a trained radiologist.

Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images.

PURPOSE: Boundary roughness of a pulmonary nodule is an important indication of its malignancy. The irregularity of the shape of a nodule is represented in terms of a few diagnostic characteristics such as spiculation, lobulation, and sphericity. Quantitative characterization of these diagnostic characteristics is essential for designing a content-based image retrieval system and computer-aided system for diagnosis of lung cancer. METHODS: This paper presents differential geometry-based techniques for computation of spiculation, lobulation, and sphericity using the binary mask of the segmented nodule. These shape features are computed in 3D considering complete nodule. RESULTS: The performance of the proposed and competing methods is evaluated in terms of the precision, mean similarity, and normalized discounted cumulative gain on 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative. The proposed methods are comparable to or better than gold standard technique. The reproducibility of proposed feature extraction techniques is evaluated using RIDER coffee break data set. The mean and standard deviation of the percent change of spiculation, lobulation, and sphericity are [Formula: see text], [Formula: see text], and [Formula: see text] %, respectively. CONCLUSION: The prior works of computation of spiculation, lobulation, and sphericity require a set of four ground truths from radiologists and, hence, can not be used in practice. The proposed methods do not require ground truth information of nodules from radiologists, and hence, it can be used in real-life computer-aided diagnosis system for lung cancer.