Liver Segmentation and Liver Cancer Detection Based on Deep Convolutional Neural Network: A Brief Bibliometric Survey

Background: This study analyzes liver segmentation and cancer detection work, with the perspectives of machine learning and deep learning and different image processing techniques from the year 2012 to 2020. The study uses different Bibliometric analysis methods. Methods: The articles on the topic were obtained from one of the most popular databases- Scopus. The year span for the analysis is considered to be from 2012 to 2020. Scopus analyzer facilitates the analysis of the databases with different categories such as documents by source, year, and county and so on. Analysis is also done by using different units of analysis such as co-authorship, co-occurrences, citation analysis etc. For this analysis Vosviewer Version 1.6.15 is used. Results: In the study, a total of 518 articles on liver segmentation and liver cancer were obtained between the years 2012 to 2020. From the statistical analysis and network analysis it can be concluded that, the maximum articles are published in the year 2020 with China is the highest contributor followed by United States and India. Conclusions: Outcome from Scoups database is 518 articles with English language has the largest number of articles. Statistical analysis is done in terms of different parameters such as Authors, documents, country, affiliation etc. The analysis clearly indicates the potential of the topic. Network analysis of different parameters is also performed. This also indicate that there is a lot of scope for further research in terms of advanced algorithms of computer vision, deep learning and machine learning

The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection

Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight

Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure

RCNN with Swallow Swarm Optimization for Liver Disease Detection and Classification

One of the most serious medical conditions that can endanger a person's life and health is liver disease. The second-leading cause of death for men and sixth-leading cause of death for women, respectively, is liver cancer. In 2008, liver cancer claimed the lives of almost 750,000 people, killing 960,000 of them. The segmentation and identification of CT images produced by computer tomography has emerged as a major topic in medical image processing. There are few choices for liver segmentation due to the enormous amount of time and resources necessary to train a deep learning model. As part of this research, we created the Region utilizing Convolutional Neural Network, a novel way of extracting the liver from CT scan images (RCNN). The suggested CNN approach, which employs softmax to isolate the liver from the background, contains of three convolutional layers and two entirely associated layers. In the CapsNet and CAL layers, there are class dependencies and an efficient mechanism to connect CAL and subsequent CapsNet processing. Finally, the classification is carried out using the SSO-CSAE model, an approach known as the swallow swarm optimization that is based on the Convolutional Sparse Autoencoder (CSAE) The MICCAI SLiver '07, 3Dircadb01, and LiTS17 benchmark datasets were used to validate the proposed RCNN-SSO approach. When compared to other frameworks, the proposed framework performed well in numerous categories

In Vivo Molecular Targeted Imaging of Cancer

Hepatocellular carcinoma (HCC) presents a global healthcare problem. It is the second most lethal cancer worldwide, causing 745,000 deaths annually. HCC accounts for 70% to 90% of primary liver cancer cases with rising incidence in developed countries. Newly diagnosed cases in the U.S. are expected to increase by 10% in three years. Symptoms of HCC typically do not appear until advanced stage, leaving surgical resection the primary therapy. However, HCC patients suffer from dire prognosis of less than 5% five-year survival rate and >50% incidence of tumor recurrence, due to poor contrast of HCC against surrounding liver tissue limiting resection accuracy. Using a molecular imaging system that targets differentially expressed tumor specific surface biomarkers may help detect HCC neoplasm missed by surgeons and preserve viable liver tissue to reduce recurrence and improve patient recovery. This dissertation presents the HCC targeting and imaging methods developed to specifically identify HCC neoplasm with high contrast, fast kinetics and deep penetration. Two cancer cell surface biomarkers, epidermal growth factor receptor (EGFR) and glypican-3 (GPC3), are important in the development of HCC. To create a molecular imaging strategy for HCC detection, short peptide sequences specifically binding to these biomarkers have been selected and validated. They demonstrated high target affinities (kd < 75 nM) and fast cellular binding kinetics (<10 min). After conjugating with near-infrared organic dye, these molecular targeting probes were able to home to the HCC tumor xenograft in vivo after intravenous administration. Ex vivo and in vivo optical imaging was conducted with fluorescent laparoscopy, whole body fluorescent imaging, and hand held dual-axis confocal microscopy. In vivo cell surface binding of peptide probe to HCC xenograft in mice was observed at subcellular resolution in both horizontal (1000×1000 µm2) and vertical (1000×430 µm2) planes. Tumor margins were automatically detected with computerized segmentation algorithm. High target-to-background ratios (2.99 and 6.2 respectively) were achieved at tumor sites after 6 and 2 hours respectively, and targeting probes were cleared from the animal system within 24 hours. In addition, targeted in vivo photoacoustic tomography (PAT) imaging visualized probe penetration inside the tumor 1.8 cm beneath intact skin. Plasmonic nanoparticles absorb light more efficiently than organic dyes. By coating GPC3 targeting peptide onto gold nanoshell (GNS) surface, in vivo photoacoustic imaging contrast was improved from 2.25 to 4.45 and imaging depth reached 2.1 cm. Peak probe uptake in vivo occurred at 2 hours and clearance took place within 12 hours, which are desirable pharmacokinetics for clinical settings of intraoperative imaging guidance. Specific binding, biodistribution and toxicity were investigated in cultured cells, ex vivo tissues (human and mouse) as well as in mouse models. The GPC3 targeting probe was able to distinguish HCC from non-HCC human patient biopsies (n=41) at 93% sensitivity and 88% specificity, with area under the receiver operator characteristic curve (AUC) value reaching 0.98. These studies showed that affinity peptide based molecular imaging is an enabling technology which will allow clinicians to perform functional imaging during surgery to identify resection margin with high contrast, sensitivity and speed.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138522/1/zhouquan_1.pd

In Vivo and In Vitro Characterization of Primary Human Liver Macrophages and Their Inflammatory State

Liver macrophages (LMs) play a central role in acute and chronic liver pathologies. Investigation of these processes in humans as well as the development of diagnostic tools and new therapeutic strategies require in vitro models that closely resemble the in vivo situation. In our study, we sought to gain further insight into the role of LMs in different liver pathologies and into their characteristics after isolation from liver tissue. For this purpose, LMs were characterized in human liver tissue sections using immunohistochemistry and bioinformatic image analysis. Isolated cells were characterized in suspension using FACS analyses and in culture using immunofluorescence staining and laser scanning microscopy as well as functional assays. The majority of our investigated liver tissues were characterized by anti-inflammatory LMs which showed a homogeneous distribution and increased cell numbers in correlation with chronic liver injuries. In contrast, pro-inflammatory LMs appeared as temporary and locally restricted reactions. Detailed characterization of isolated macrophages revealed a complex disease dependent pattern of LMs consisting of pro- and anti-inflammatory macrophages of different origins, regulatory macrophages and monocytes. Our study showed that in most cases the macrophage pattern can be transferred in adherent cultures. The observed exceptions were restricted to LMs with pro-inflammatory characteristics