TY - GEN
T1 - Semi-supervised Contrastive Learning for White Blood Cell Segmentation from Label-free Quantitative Phase Imaging
AU - Sengupta, Sourya
AU - Fanous, Michael
AU - Li, Hua
AU - Anastasio, Mark A.
N1 - Publisher Copyright:
© 2023 SPIE.
PY - 2023
Y1 - 2023
N2 - Quantitative phase imaging (QPI) is an advanced label-free imaging technique that can quantify pathlength changes in biological samples at the nanometer scale without staining or tagging. White blood cells (WBCs) are the key components of the body's immune system. However, automatic detection and segmentation of WBCs from QPI data can be challenging as QPI may lack the needed intrinsic specificity and contrast compared to a stained brightfield image. Morever, typical supervised learning methods require a large number of annotations for model training which involves time-consuming efforts and clinical expertise, and may not be available often. Motivated by the effectiveness of the recently developed contrastive learning-based semi-supervised segmentation methods and considering the intrinsic properties of QPI, the main contribution of this work is to implement an efficient semi-supervised segmentation method to segment WBCs from QPI data when only a small amount of ground-truth labels are available. A DeeplabV3+ and DeeplabV2 architecture were used as a backbone of the segmentation network with a modified convolution module, specifically to capture the subtle morphological details of WBCs in QPI. The preliminary results showed significant improvement in terms of quantitative metrics compared to the supervised training methods. An exploratory study was also performed to investigate the effect of the contrastive learning module on segmentation performance. To the best of our knowledge, this is the first application of the semi-supervised contrastive learning method in the WBC segmentation framework from QPI.
AB - Quantitative phase imaging (QPI) is an advanced label-free imaging technique that can quantify pathlength changes in biological samples at the nanometer scale without staining or tagging. White blood cells (WBCs) are the key components of the body's immune system. However, automatic detection and segmentation of WBCs from QPI data can be challenging as QPI may lack the needed intrinsic specificity and contrast compared to a stained brightfield image. Morever, typical supervised learning methods require a large number of annotations for model training which involves time-consuming efforts and clinical expertise, and may not be available often. Motivated by the effectiveness of the recently developed contrastive learning-based semi-supervised segmentation methods and considering the intrinsic properties of QPI, the main contribution of this work is to implement an efficient semi-supervised segmentation method to segment WBCs from QPI data when only a small amount of ground-truth labels are available. A DeeplabV3+ and DeeplabV2 architecture were used as a backbone of the segmentation network with a modified convolution module, specifically to capture the subtle morphological details of WBCs in QPI. The preliminary results showed significant improvement in terms of quantitative metrics compared to the supervised training methods. An exploratory study was also performed to investigate the effect of the contrastive learning module on segmentation performance. To the best of our knowledge, this is the first application of the semi-supervised contrastive learning method in the WBC segmentation framework from QPI.
KW - Contrastive learning
KW - Deep learning
KW - Quantitative phase imaging
KW - Semi-supervised segmentation
UR - http://www.scopus.com/inward/record.url?scp=85160559645&partnerID=8YFLogxK
U2 - 10.1117/12.2654584
DO - 10.1117/12.2654584
M3 - Conference contribution
AN - SCOPUS:85160559645
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2023
A2 - Tomaszewski, John E.
A2 - Ward, Aaron D.
PB - SPIE
T2 - Medical Imaging 2023: Digital and Computational Pathology
Y2 - 19 February 2023 through 23 February 2023
ER -