Image segmentation evaluation: a survey of methods

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

Gastric cancer is a common and deadly cancer in the world. The gold standard for the detection of gastric cancer is the histological examination by pathologists, where Gastric Histopathological Image Analysis (GHIA) contributes significant diagnostic information. The histopathological images of gastric cancer contain sufficient characterization information, which plays a crucial role in the diagnosis and treatment of gastric cancer. In order to improve the accuracy and objectivity of GHIA, Computer-Aided Diagnosis (CAD) has been widely used in histological image analysis of gastric cancer. In this review, the CAD technique on pathological images of gastric cancer is summarized. Firstly, the paper summarizes the image preprocessing methods, then introduces the methods of feature extraction, and then generalizes the existing segmentation and classification techniques. Finally, these techniques are systematically introduced and analyzed for the convenience of future researchers.

Deep learning classification of lipid droplets in quantitative phase images

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.

Can artificial intelligence overtake human intelligence on the bumpy road towards glioma therapy?

Gliomas are one of the most devastating primary brain tumors which impose significant management challenges to the clinicians. The aggressive behaviour of gliomas is mainly attributed to their rapid proliferation, unravelled genomics and the blood-brain barrier which protects the tumor cells from chemotherapeutic regimens. Suspects of brain tumors are usually assessed by magnetic resonance imaging and computed tomography. These images allow surgeons to decide on the tumor grading, intra-operative pathology, feasibility of surgery, and treatment planning. All these data are compiled manually by physicians, wherein it takes time for the validation of results and concluding the treatment modality. In this context, the arrival of artificial intelligence in this era of personalized medicine, has proven promising performance in the diagnosis and management of gliomas. Starting from grading prediction till outcome evaluation, artificial intelligence-based forefronts have revolutionized oncological research. Interestingly, this approach has also been able to precisely differentiate tumor lesion from healthy tissues. However, till date, their utility in neuro-oncological field remains limited due to the issues pertaining to their reliability and transparency. Hence, to shed novel insights on the "clinical utility of this novel approach on glioma management" and to reveal "the black-boxes that have to be solved for fruitful application of artificial intelligence in neuro-oncology research", we provide in this review, a succinct description of the potential gear of artificial intelligence-based avenues in glioma treatment and the barriers that impede their rapid implementation in neuro-oncology.

Radiogenomics in Colorectal Cancer

Simple Summary

Colorectal carcinoma is characterized by intratumoral heterogeneity that can be assessed by radiogenomics. Radiomics, high-throughput quantitative data extracted from medical imaging, combined with molecular analysis, through genomic and transcriptomic data, is expected to lead to significant advances in personalized medicine. However, a radiogenomics approach in colorectal cancer is still in its early stages and many problems remain to be solved. Here we review the progress and challenges in this field at its current stage, as well as future developments.

Abstract

The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features—radiomics and genetic profile modifications—genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.

Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach

The evaluation of remote sensing imagery segmentation results plays an important role in the further image analysis and decision-making. The search for the optimal segmentation method for a particular data set and the suitability of segmentation results for the use in satellite image classification are examples where the proper image segmentation quality assessment can affect the quality of the final result. There is no extensive research related to the assessment of the segmentation effectiveness of the images. The designed objective quality assessment metrics that can be used to assess the quality of the obtained segmentation results usually take into account the subjective features of the human visual system (HVS). A novel approach is used in the article to estimate the effectiveness of satellite image segmentation by relating and determining the correlation between subjective and objective segmentation quality metrics. Pearson’s and Spearman’s correlation was used for satellite images after applying a k-means++ clustering algorithm based on colour information. Simultaneously, the dataset of the satellite images with ground truth (GT) based on the “DeepGlobe Land Cover Classification Challenge” dataset was constructed for testing three classes of quality metrics for satellite image segmentation.