Locally constrained active contour: a region-based level set for ovarian cancer metastasis segmentation

2014 ◽  
Author(s):  
Jianfei Liu ◽  
Jianhua Yao ◽  
Shijun Wang ◽  
Marius George Linguraru ◽  
Ronald M. Summers
Keyword(s):  
Ovarian Cancer ◽  
Level Set ◽  
Active Contour ◽  
Cancer Metastasis

scholarly journals LncRNA SPOCD1-AS from ovarian cancer extracellular vesicles remodels mesothelial cells to promote peritoneal metastasis via interacting with G3BP1

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Conghui Wang ◽  
Jiaying Wang ◽  
Xiameng Shen ◽  
Mingyue Li ◽  
Yongfang Yue ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Extracellular Vesicles ◽  
Peritoneal Metastasis ◽  
Cancer Metastasis ◽  
Cancer Therapeutics ◽  
Mesothelial Cells ◽  
Ovarian Cancer Cells ◽  
Adhesion Assay ◽  
Mesenchymal Transition

Abstract Background Metastasis is the key cause of death in ovarian cancer patients. To figure out the biological nature of cancer metastasis is essential for developing effective targeted therapy. Here we investigate how long non-coding RNA (lncRNA) SPOCD1-AS from ovarian cancer extracellular vesicles (EVs) remodel mesothelial cells through a mesothelial-to-mesenchymal transition (MMT) manner and facilitate peritoneal metastasis. Methods EVs purified from ovarian cancer cells and ascites of patients were applied to mesothelial cells. The MMT process of mesothelial cells was assessed by morphology observation, western blot analysis, migration assay and adhesion assay. Altered lncRNAs of EV-treated mesothelial cells were screened by RNA sequencing and identified by qRT-PCR. SPOCD1-AS was overexpressed or silenced by overexpression lentivirus or shRNA, respectively. RNA pull-down and RNA immunoprecipitation assays were conducted to reveal the mechanism by which SPOCD1-AS remodeled mesothelial cells. Interfering peptides were synthesized and applied. Ovarian cancer orthotopic implantation mouse model was established in vivo. Results We found that ovarian cancer-secreted EVs could be taken into recipient mesothelial cells, induce the MMT phenotype and enhance cancer cell adhesion to mesothelial cells. Furthermore, SPOCD1-AS embedded in ovarian cancer-secreted EVs was transmitted to mesothelial cells to induce the MMT process and facilitate peritoneal colonization in vitro and in vivo. SPOCD1-AS induced the MMT process of mesothelial cells via interacting with G3BP1 protein. Additionally, G3BP1 interfering peptide based on the F380/F382 residues was able to block SPOCD1-AS/G3BP1 interaction, inhibit the MMT phenotype of mesothelial cells, and diminish peritoneal metastasis in vivo. Conclusions Our findings elucidate the mechanism associated with EVs and their cargos in ovarian cancer peritoneal metastasis and may provide a potential approach for metastatic ovarian cancer therapeutics.

scholarly journals CD133 Promotes Adhesion to the Ovarian Cancer Metastatic Niche

2018 ◽  
Vol 11 ◽  
pp. 117906441876788 ◽  
Author(s):  
Lynn Roy ◽  
Alexander Bobbs ◽  
Rachel Sattler ◽  
Jeffrey L Kurkewich ◽  
Paige B Dausinas ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Metastasis ◽  
Ex Vivo ◽  
Surface Protein ◽  
Ovarian Cancer Cells ◽  
Metastatic Niche ◽  
Attractive Therapeutic Target ◽  
Peritoneal Mesothelium

Cancer stem cells (CSCs) are an attractive therapeutic target due to their predicted role in both metastasis and chemoresistance. One of the most commonly agreed on markers for ovarian CSCs is the cell surface protein CD133. CD133+ ovarian CSCs have increased tumorigenicity, resistance to chemotherapy, and increased metastasis. Therefore, we were interested in defining how CD133 is regulated and whether it has a role in tumor metastasis. Previously we found that overexpression of the transcription factor, ARID3B, increased the expression of PROM1 (CD133 gene) in ovarian cancer cells in vitro and in xenograft tumors. We report that ARID3B directly regulates PROM1 expression. Importantly, in a xenograft mouse model of ovarian cancer, knockdown of PROM1 in cells expressing exogenous ARID3B resulted in increased survival time compared with cells expressing ARID3B and a control short hairpin RNA. This indicated that ARID3B regulation of PROM1 is critical for tumor growth. Moreover, we hypothesized that CD133 may affect metastatic spread. Given that the peritoneal mesothelium is a major site of ovarian cancer metastasis, we explored the role of PROM1 in mesothelial attachment. PROM1 expression increased adhesion to mesothelium in vitro and ex vivo. Collectively, our work demonstrates that ARID3B regulates PROM1 adhesion to the ovarian cancer metastatic niche.

scholarly journals On the Relationship between Variational Level Set-Based and SOM-Based Active Contours

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Mohammed M. Abdelsamea ◽  
Giorgio Gnecco ◽  
Mohamed Medhat Gaber ◽  
Eyad Elyan
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contours ◽  
Level Set Methods ◽  
Energy Functional ◽  
Self Organizing Maps ◽  
Complex Shapes ◽  
Variational Level Set ◽  
Preservation Property ◽  
The Relationship

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods have been used to build an active contour with the aim of modeling arbitrarily complex shapes. Moreover, they can handle also topological changes of the contours. Self-Organizing Maps (SOMs) have attracted the attention of many computer vision scientists, particularly in modeling an active contour based on the idea of utilizing the prototypes (weights) of a SOM to control the evolution of the contour. SOM-based models have been proposed in general with the aim of exploiting the specific ability of SOMs to learn the edge-map information via their topology preservation property and overcoming some drawbacks of other ACMs, such as trapping into local minima of the image energy functional to be minimized in such models. In this survey, we illustrate the main concepts of variational level set-based ACMs, SOM-based ACMs, and their relationship and review in a comprehensive fashion the development of their state-of-the-art models from a machine learning perspective, with a focus on their strengths and weaknesses.

Abstract 2461: SIRT1 regulates ovarian cancer metastasis via altering the exosome release and glycogen deposition

2021 ◽  
Author(s):  
Sraddhya Roy ◽  
Ananya Das ◽  
Manisha Vernekar ◽  
Subhadip Das ◽  
Nabanita Chatterjee
Keyword(s):  
Ovarian Cancer ◽  
Cancer Metastasis ◽  
Glycogen Deposition

Superoxide Dismutase 2 is Necessary for Key Steps in the Transcoelomic Route of Ovarian Cancer Metastasis

2013 ◽  
Vol 65 ◽  
pp. S20
Author(s):  
Madhubhani LP Hemachandra ◽  
Usawadee Dier ◽  
Larissa M Uusitalo ◽  
Donghui Shin ◽  
Sarah a Engelberth ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Superoxide Dismutase ◽  
Cancer Metastasis ◽  
Key Steps ◽  
Superoxide Dismutase 2

SEGMENTATION OF ABDOMEN DISEASES USING ACTIVE CONTOUR MODELS IN CT IMAGES

2015 ◽  
Vol 27 (05) ◽  
pp. 1550047 ◽  
Author(s):  
Gaurav Sethi ◽  
B. S. Saini
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contour Model ◽  
Diagnostic System ◽  
Ct Images ◽  
Active Contour Models ◽  
Segmentation Methods ◽  
Contour Models ◽  
Edge Based ◽  
Level Set Evolution

Precise segmentation of abdomen diseases like tumor, cyst and stone are crucial in the design of a computer aided diagnostic system. The complexity of shapes and similarity of texture of disease with the surrounding tissues makes the segmentation of abdomen related diseases much more challenging. Thus, this paper is devoted to the segmentation of abdomen diseases using active contour models. The active contour models are formulated using the level-set method. Edge-based Distance Regularized Level Set Evolution (DRLSE) and region based Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) are used for segmentation of various abdomen diseases. These segmentation methods are applied on 60 CT images (20 images each of tumor, cyst and stone). Comparative analysis shows that edge-based active contour models are able to segment abdomen disease more accurately than region-based level set active contour model.

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