Automatic Detection of Rouleau Formations Using Image Processing and Deep Learning
(2021) In Master’s Theses in Mathematical Sciences FMAM05 20211Mathematics (Faculty of Engineering)
- Abstract
- Analysis of peripheral blood smears is commonly used to aid physicians in diagnosing diseases, checking for infections and evaluating the function of organs. One object of interest in such analysis is the presence of red blood cell aggregates called rouleaux. CellaVision's product currently automates the traditionally resource-intensive process of blood smear analysis, which leads to improved patient value. As of now, it lacks the function of detecting rouleaux formations.
The aim of this thesis project is to investigate the addition of this feature to CellaVision's product. For this aim, we have implemented and compared two object detection architectures, employing image processing and machine learning
methods. One of the... (More) - Analysis of peripheral blood smears is commonly used to aid physicians in diagnosing diseases, checking for infections and evaluating the function of organs. One object of interest in such analysis is the presence of red blood cell aggregates called rouleaux. CellaVision's product currently automates the traditionally resource-intensive process of blood smear analysis, which leads to improved patient value. As of now, it lacks the function of detecting rouleaux formations.
The aim of this thesis project is to investigate the addition of this feature to CellaVision's product. For this aim, we have implemented and compared two object detection architectures, employing image processing and machine learning
methods. One of the architectures, called Single Shot MultiBox Detector, has previously been used by CellaVision for similar tasks. The other was tailormade for the task of detecting rouleaux, based on the method of region proposals employed in various other object detection architectures. We have also collected and annotated the data necessary to train and evaluate models using these architectures. Said models have been tested and evaluated on expert-annotated data for different sizes of rouleau formations.
We found that the implemented models are capable of detecting rouleaux in blood smear images collected by CellaVision's systems. However, results are not robust enough for use in production, and the defnitive performance of the models requires further investigation by experts. Future work is needed to ensure accurate model evaluation and suffcient performance for use in CellaVision's product. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9066480
- author
- Johansson, Josefin LU and König, Oscar LU
- supervisor
- organization
- course
- FMAM05 20211
- year
- 2021
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- object detection, image processing, deep learning, rouleaux, rouleaux detection
- publication/series
- Master’s Theses in Mathematical Sciences
- report number
- LUTFMA-3454-2021
- ISSN
- 1404-6342
- other publication id
- 2021:E39
- language
- English
- id
- 9066480
- date added to LUP
- 2021-10-13 16:12:11
- date last changed
- 2021-10-13 16:12:11
@misc{9066480,
abstract = {{Analysis of peripheral blood smears is commonly used to aid physicians in diagnosing diseases, checking for infections and evaluating the function of organs. One object of interest in such analysis is the presence of red blood cell aggregates called rouleaux. CellaVision's product currently automates the traditionally resource-intensive process of blood smear analysis, which leads to improved patient value. As of now, it lacks the function of detecting rouleaux formations.
The aim of this thesis project is to investigate the addition of this feature to CellaVision's product. For this aim, we have implemented and compared two object detection architectures, employing image processing and machine learning
methods. One of the architectures, called Single Shot MultiBox Detector, has previously been used by CellaVision for similar tasks. The other was tailormade for the task of detecting rouleaux, based on the method of region proposals employed in various other object detection architectures. We have also collected and annotated the data necessary to train and evaluate models using these architectures. Said models have been tested and evaluated on expert-annotated data for different sizes of rouleau formations.
We found that the implemented models are capable of detecting rouleaux in blood smear images collected by CellaVision's systems. However, results are not robust enough for use in production, and the defnitive performance of the models requires further investigation by experts. Future work is needed to ensure accurate model evaluation and suffcient performance for use in CellaVision's product.}},
author = {{Johansson, Josefin and König, Oscar}},
issn = {{1404-6342}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master’s Theses in Mathematical Sciences}},
title = {{Automatic Detection of Rouleau Formations Using Image Processing and Deep Learning}},
year = {{2021}},
}