Design of Metaheuristic Optimization-Based Vascular Segmentation Techniques for Photoacoustic Images

Vascular regional analysis unveils differential responses to anti-angiogenic therapy in pancreatic xenografts through macroscopic photoacoustic imaging

Background: Amongst the various imaging techniques that provide surrogate tumor radiographic indications to aid in planning, monitoring, and predicting outcomes of therapy, ultrasound-guided photoacoustic imaging (US-PAI) is a promising non-ionizing modality based on endogenous blood (hemoglobin) and blood oxygen saturation (StO₂) contrast. Adaptation of US-PAI to the clinical realm requires macroscopic system configurations for adequate depth visualization. Methods: Here we present a vascular regional analysis (VRA) methodology of obtaining areas of low and high vessel density regions within the tumor (LVD and HVD respectively) by frequency domain filtering of macroscopic PA images. In this work, we evaluated the various vascular and oxygenation profiles of different murine xenografts of pancreatic cancer (AsPC-1, MIA PaCa-2, and BxPC-3) that have varying levels of angiogenic potentials and investigated the effects of receptor tyrosine kinase inhibitor (sunitinib) on the tumor microvessel density and StO₂. Results: The administration of sunitinib resulted in transient deoxygenation and reduction in vessel density within 72 h in two (AsPC-1 and MIA PaCa-2) of the three tumor types. Utilizing VRA, the regional change in StO2 (∆StO2) revealed the preferential targeting of sunitinib in LVD regions in only the AsPC-1 tumors. We also identified the presence of vascular normalization (validated through immunohistochemistry) in the sunitinib treated AsPC-1 tumors at day 8 post-treatment where a significant increases in HVD ∆StO2 (~20%) were seen following the 72-hour time point, indicative of improved vessel flow and functionality. Treated AsPC-1 vasculature displayed increased maturity and functionality compared to non-treated tumors on day 8, while these same metrics showed no conclusive evidence of vascular normalization in MIA PaCa-2 or BxPC-3 tumors. Conclusion: Overall, VRA as a tool to monitor treatment response allowed us to identify time points of vascular remodeling, highlighting its ability to provide insights into the tumor microenvironment for sunitinib treatment and other anti-angiogenic therapies.

Deep Learning Approaches for Automatic Localization in Medical Images

Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy. These achievements have shown great promise in a wide variety of fields, especially in medical image analysis by creating opportunities to diagnose and treat diseases earlier. In recent years, the application of the DNN for object localization has gained the attention of researchers due to its success over conventional methods, especially in object localization. As this has become a very broad and rapidly growing field, this study presents a short review of DNN implementation for medical images and validates its efficacy on benchmarks. This study presents the first review that focuses on object localization using the DNN in medical images. The key aim of this study was to summarize the recent studies based on the DNN for medical image localization and to highlight the research gaps that can provide worthwhile ideas to shape future research related to object localization tasks. It starts with an overview on the importance of medical image analysis and existing technology in this space. The discussion then proceeds to the dominant DNN utilized in the current literature. Finally, we conclude by discussing the challenges associated with the application of the DNN for medical image localization which can drive further studies in identifying potential future developments in the relevant field of study.

Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images

Melanoma is a kind of skin cancer caused by the irregular development of pigment-producing cells. Since melanoma detection efficiency is limited to different factors such as poor contrast among lesions and nearby skin regions, and visual resemblance among melanoma and non-melanoma lesions, intelligent computer-aided diagnosis (CAD) models are essential. Recently, computational intelligence (CI) and deep learning (DL) techniques are utilized for effective decision-making in the biomedical field. In addition, the fast-growing advancements in computer-aided surgeries and recent progress in molecular, cellular, and tissue engineering research have made CI an inevitable part of biomedical applications. In this view, the research work here develops a novel computational intelligence-based melanoma detection and classification technique using dermoscopic images (CIMDC-DIs). The proposed CIMDC-DI model encompasses different subprocesses. Primarily, bilateral filtering with fuzzy k-means (FKM) clustering-based image segmentation is applied as a preprocessing step. Besides, NasNet-based feature extractor with stochastic gradient descent is applied for feature extraction. Finally, the manta ray foraging optimization (MRFO) algorithm with a cascaded neural network (CNN) is exploited for the classification process. To ensure the potential efficiency of the CIMDC-DI technique, we conducted a wide-ranging simulation analysis, and the results reported its effectiveness over the existing recent algorithms with the maximum accuracy of 97.50%.