Gao, X., & Zhu, P., & Ferdjallah, M. (2025, March), Applications of Computer Vision Segmentation in Hematology and Blood Cell Medical Imaging  Paper presented at 2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia. 10.18260/1-2--54650

\bibitem{asee_peer_54650}
  Gao, X., \& Zhu, P., \& Ferdjallah, M. (2025, March), \emph{Applications of Computer Vision Segmentation in Hematology and Blood Cell Medical Imaging}
  Paper presented at 2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia.
  10.18260/1-2--54650

Xu Gao, Pingping Zhu, and Mohammed Ferdjallah.  "Applications of Computer Vision Segmentation in Hematology and Blood Cell Medical Imaging".  2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia, 2025, March.  ASEE Conferences, 2025.  https://peer.asee.org/54650 Internet.  15 Jun, 2025

\bibitem{asee_peer_54650}
  Xu Gao, Pingping Zhu, and Mohammed Ferdjallah.
  "Applications of Computer Vision Segmentation in Hematology and Blood Cell Medical Imaging".
  \emph{2025 ASEE North Central Section  (NCS) Annual Conference, Marshall University, Huntington, West Virginia, 2025, March}.
  ASEE Conferences, 2025.
  https://peer.asee.org/54650 Internet.  15 Jun, 2025

@INPROCEEDINGS{asee_peer_54650
author = "Xu Gao, Pingping Zhu, and Mohammed Ferdjallah"
title = "Applications of Computer Vision Segmentation in Hematology and Blood Cell Medical Imaging"
booktitle = "2025 ASEE North Central Section  (NCS) Annual Conference"
year = "2025"
month = "March"
address = "Marshall University, Huntington, West Virginia"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/54650}
number = {10.18260/1-2--54650}
}

TY  - CPAPER
AB  - Medical image segmentation is crucial in diagnostics and treatment planning. It enables precise identification of structures within medical images, which is essential for accurate analysis and decision-making. However, many researchers and practitioners in the medical field face challenges with the technical coding skills needed to leverage deep learning models effectively. There is a strong need for accessible, straightforward tools that allow medical professionals to integrate state-of-the-art segmentation models into their workflows with minimal coding. This study aims to provide medical researchers with a practical guide to using deep learning models for image segmentation, empowering them to engage confidently with machine learning techniques. In this study, we presented three prominent segmentation models SegNet, U-Net, and YOLO-Seg. We also outlined their unique advantages. Each model offers specific benefits such as accuracy and processing speed. We provided statistical evidence to help practitioners select the most suitable model for their needs. Furthermore, we explored their applications using blood cell image datasets, showcasing their implementation within a medical context. While this study focuses on blood cell images, the methods discussed are versatile and can be extended to other types of medical images, such as MRI scans for brain tumor segmentation, CT scans for organ and lesion identification, X-rays for lung disease detection, and ultrasound images for fetal and cardiac analysis.
AU  - Xu Gao
AU  - Pingping Zhu
AU  - Mohammed Ferdjallah
CY  - Marshall University, Huntington, West Virginia
DA  - 2025/03/28
PB  - ASEE Conferences
TI  - Applications of Computer Vision Segmentation in Hematology and Blood Cell Medical Imaging
UR  - https://peer.asee.org/54650
DO  - 10.18260/1-2--54650
ER  -