Artificial Intelligence in Regenerative Medicine: Applications and Implications.

The field of regenerative medicine is constantly advancing and aims to repair, regenerate, or substitute impaired or unhealthy tissues and organs using cutting-edge approaches such as stem cell-based therapies, gene therapy, and tissue engineering. Nevertheless, incorporating artificial intelligence (AI) technologies has opened new doors for research in this field. AI refers to the ability of machines to perform tasks that typically require human intelligence in ways such as learning the patterns in the data and applying that to the new data without being explicitly programmed.

Exploring SARS-COV-2 structural proteins to design a multi-epitope vaccine using immunoinformatics approach: An in silico study.

In December 2019, a new virus called SARS-CoV-2 was reported in China and quickly spread to other parts of the world. The development of SARS-COV-2 vaccines has recently received much attention from numerous researchers. The present study aims to design an effective multi-epitope vaccine against SARS-COV-2 using the reverse vaccinology method.

Clinically-inspired automatic classification of ovarian carcinoma subtypes.

Context: It has been shown that ovarian carcinoma subtypes are distinct pathologic entities with differing prognostic and therapeutic implications. Histotyping by pathologists has good reproducibility, but occasional cases are challenging and require immunohistochemistry and subspecialty consultation. Motivated by the need for more accurate and reproducible diagnoses and to facilitate pathologists' workflow, we propose an automatic framework for ovarian carcinoma classification.

Endoscopic scene labelling and augmentation using intraoperative pulsatile motion and colour appearance cues with preoperative anatomical priors.

Purpose: Despite great advances in medical image segmentation, the accurate and automatic segmentation of endoscopic scenes remains a challenging problem. Two important aspects have to be considered in segmenting an endoscopic scene: (1) noise and clutter due to light reflection and smoke from cutting tissue, and (2) structure occlusion (e.g.

Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells.

In this paper, we introduce and evaluate the systems submitted to the first Overlapping Cervical Cytology Image Segmentation Challenge, held in conjunction with the IEEE International Symposium on Biomedical Imaging 2014. This challenge was organized to encourage the development and benchmarking of techniques capable of segmenting individual cells from overlapping cellular clumps in cervical cytology images, which is a prerequisite for the development of the next generation of computer-aided diagnosis systems for cervical cancer. In particular, these automated systems must detect and accurately segment both the nucleus and cytoplasm of each cell, even when they are clumped together and, hence, partially occluded.

Simultaneous Multi-Structure Segmentation and 3D Nonrigid Pose Estimation in Image-Guided Robotic Surgery.

In image-guided robotic surgery, segmenting the endoscopic video stream into meaningful parts provides important contextual information that surgeons can exploit to enhance their perception of the surgical scene. This information provides surgeons with real-time decision-making guidance before initiating critical tasks such as tissue cutting. Segmenting endoscopic video is a challenging problem due to a variety of complications including significant noise attributed to bleeding and smoke from cutting, poor appearance contrast between different tissue types, occluding surgical tools, and limited visibility of the objects' geometries on the projected camera views.

Efficient multi-organ segmentation in multi-view endoscopic videos using pre-operative priors.

Synergistic fusion of pre-operative (pre-op) and intraoperative (intra-op) imaging data provides surgeons with invaluable insightful information that can improve their decision-making during minimally invasive robotic surgery. In this paper, we propose an efficient technique to segment multiple objects in intra-op multi-view endoscopic videos based on priors captured from pre-op data. Our approach leverages information from 3D pre-op data into the analysis of visual cues in the 2D intra-op data by formulating the problem as one of finding the 3D pose and non-rigid deformations of tissue models driven by features from 2D images.

Local optimization based segmentation of spatially-recurring, multi-region objects with part configuration constraints.

Incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate and plausible results. Two important constraints, containment and exclusion of regions, have gained attention in recent years mainly due to their descriptive power. In this paper, we augment the level set framework with the ability to handle these two intuitive geometric relationships, containment and exclusion, along with a distance constraint between boundaries of multi-region objects.

Segmentation of cells with partial occlusion and part configuration constraint using evolutionary computation.

We propose a method for targeted segmentation that identifies and delineates only those spatially-recurring objects that conform to specific geometrical, topological and appearance priors. By adopting a "tribes"-based, global genetic algorithm, we show how we incorporate such priors into a faithful objective function unconcerned about its convexity. We evaluated our framework on a variety of histology and microscopy images to segment potentially overlapping cells with complex topology.