KEYWORDS: Image segmentation, Education and training, Image quality, Medical imaging, Image classification, Cancer, Deep learning, Cervix, Cancer detection, Data modeling
With the technological advancements in machine learning, it has become more prevalent to use learning techniques for clinical decision-making based on medical images. One of the state-of-the-art methods used for this purpose is Convolutional Neural Networks (CNN) for medical image segmentation and deep learning models for disease detection and classification. In this paper, we propose a framework for image segmentation using hierarchical CNNs to classify different types of cells using small frame images. This paper aims to generalize the segmentation of cancer cells, starting with cervix cancer. The first step of the framework is to achieve automatic nucleus and cell masking of the images using U-Net. The images are then segmented into “satisfactory” and “unsatisfactory” categories to determine whether these images can be used in our classification model. Using the hierarchical CNN, the satisfactory images are clustered based on cell types since the cell features that need to be considered vary between different cell types. Lastly, our classification model is trained with automatically segmented images to classify different cancer types based on cell images using various features, such as the area of the nucleus, the ratio of the nucleus area and cytoplasm area and the visual morphology of chromatin strands in the nucleus. To demonstrate the performance of the proposed framework, a labeled dataset, taken from the Detay Pathology and Cytology Laboratory, with over 100 images were used.
Modern deep learning-based cell segmentation algorithms with high computational power have enabled automated and high-throughput segmentation of bacteria. Previous studies, relying on either manual or automated cell segmentation approaches, have proved that a clonal bacteria population cultured in a regular growth medium in a homogenous microenvironment exhibits heterogeneity. When antibiotic treatment has been applied, heterogeneity of the bacteria population may increase depending on the working mechanisms of an administrated antibiotic. Therefore, important features of rare cells, such as asymmetric division of antibiotic persister cells or cells with metastable phenotypes, might be masked by heterogeneity of a population, particularly when a limited number of the cells was analyzed. Therefore, automated image segmentation and analysis approaches have significantly impact on accurate, rapid, and reliable feature identification, particularly for extracting quantitative high-resolution data at high throughput. Here, we implemented U-Net algorithm for segmentation of Escherichia coli cells in the absence and presence of ciprofloxacin. The accuracy values were 0.9912 and 0.9869 for the control and ciprofloxacin-treated cell populations, respectively. Next, we developed an algorithm using Phyton and the OpenCV library to extract the cell number, cellular area, and solidity features of the cells. We believe that our preliminary data might contribute to development of automated, reliable, accurate, and bacteria or antibiotic specific image segmentation tools.
Glioblastoma multiforme (GBM) is one of the most aggressive primary brain tumors with its extreme proliferation and invasiveness. U87 human glioma cell line is one of the best representative cell lines for GBM with its extremely heterogenous and frequently altered morphologies. Quantification of heterogeneity and morphological changes of U87 glioma cells are mostly based on manual analysis. Therefore, automated image segmentation and analysis approaches are crucial. Here, we implemented U-Net algorithm for segmentation of U87 glioma cells and obtained 0.06% loss and 97.3% accuracy values. Next, we integrated Chan-Vese, K-means, and Morphological Filtering. Finally, we compared the performances of these approaches. We believe that our preliminary data might contribute to development of automated, reliable, accurate, and cell type specific image segmentation tools.
Hepatocellular carcinoma is one of the most common types of liver cancer causing death all over the world. Although early-stage liver cancer can sometimes be treated with partial hepatectomy, liver transplantation, ablation, and embolization, sorafenib treatment is the only approved systemic therapy for advanced HCC. The aim of this research is to develop tools and methods to understand the individuality of hepatocellular carcinoma. Microfluidic cell-culture platform has been developed to observe behavior of single-cells; fluorescence microscopy has been implemented to investigate phenotypic changes of cells. Our preliminary data proved high-level heterogeneity of hepatocellular carcinoma while verifying limited growth of liver cancer cell lines on the silicon wafer.
Conference Committee Involvement (5)
Emerging Topics in Artificial Intelligence (ETAI) 2025
3 August 2025 | San Diego, California, United States
Emerging Topics in Artificial Intelligence (ETAI) 2024
18 August 2024 | San Diego, California, United States
Emerging Topics in Artificial Intelligence (ETAI) 2023
20 August 2023 | San Diego, California, United States
Emerging Topics in Artificial Intelligence (ETAI) 2022
21 August 2022 | San Diego, California, United States
Emerging Topics in Artificial Intelligence (ETAI) 2021
1 August 2021 | San Diego, California, United States
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