TY  - JOUR
TI  - Improved cell segmentation using deep learning in label-free optical microscopy
images
AB  - The recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional
methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for
further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to
overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the
decoder. This alteration makes the model superconvergent yielding improved performance results on two challenging
optical microscopy image series: a phase-contrast dataset of our own (MDA-MB-231) and a brightfield dataset from a
well-known challenge (DSB2018). We utilized the U-Net with pretrained ResNet-18 as the encoder for the segmentation
task. Hence, following the modifications, we redesign a novel skip-connection to reduce the semantic gap between the
encoder and the decoder. The proposed skip-connection increases the accuracy of the model on both datasets. The
proposed segmentation approach results in Jaccard Index values of 85.0% and 89.2% on the DSB2018 and MDA-MB-231
datasets, respectively. The results reveal that our method achieves competitive results compared to the state-of-the-art
approaches and surpasses the performance of baseline approaches.
AU  - Ayanzadeh, Aydin
AU  - Onal, Sevgi
AU  - Toreyin, Behcet Ugur
AU  - Unay, Devrim
AU  - Yalcin-Ozuysal, Ozden
AU  - Pesen Okvur, Devrim
DO  - 10.3906/elk-2105-244
PY  - 2021
JO  - Turkish Journal of Electrical Engineering and Computer Sciences
VL  - 29
IS  - Özel sayı 1
SN  - 1300-0632
SP  - 2855
EP  - 2868
DB  - TRDizin
UR  - http://search/yayin/detay/526977
ER  -