DOI: 10.5061/dryad.6q573n637
title: Telecentric wide-field reflected light microscopic dataset

description: Multi-class segmentation of unlabelled living cells in time-lapse light
 microscopy images is challenging due to the temporal behaviour and changes
 in cell life cycles and the complexity of images of this kind. The
 deep-learning-based methods achieved promising outcomes and remarkable
 success in single- and multi-class medical and microscopy image
 segmentation. The main objective of this study is to develop a hybrid
 deep-learning-based categorical segmentation and classification method for
 living HeLa cells in reflected light microscopy images. A symmetric simple
 U-Net and three asymmetric hybrid convolution neural
 networks---VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net were proposed
 and mutually compared to find the most suitable architecture for
 multi-class segmentation of our datasets.  The inception module
 in the Inception-U-Net contained kernels with different sizes within the
 same layer to extract all feature descriptors. The series of residual
 blocks with the skip connections in each ResNet34-U-Net's level
 alleviated the gradient vanishing problem and improved the generalisation
 ability. The m-IoU scores of multi-class segmentation for our datasets
 reached 0.7062, 0.7178, 0.7907, and 0.8067 for the simple U-Net,
 VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net, respectively. For each
 class and the mean value across all classes, the most accurate multi-class
 semantic segmentation was achieved using the ResNet34-U-Net architecture
 (evaluated as the m-IoU and Dice metrics).
description: # Telecentric wide-field reflected light microscopic dataset This
 Telecentric bright-field_README.txt file was generated on 2023-04-20 by
 Ali Ghaznavi GENERAL INFORMATION 1. Symmetry Breaking in the U-Net: Hybrid
 Deep-Learning Multi-Class Segmentation of HeLa Cells in Reflected Light
 Microscopy Images 2. Author Information First-author, Corresponding author
 Name: MSc. Ali Ghaznavi Institution: Institute of Complex Systems,
 University of South Bohemia in České Budějovice, Zámek 136, 373 33, Nové
 Hrady, Czech Republic Email: Co-author Name:
 Dr. Renata Rychtáriková Institution: Institute of Complex Systems,
 University of South Bohemia in České Budějovice, Zámek 136, 373 33, Nové
 Hrady, Czech Republic Email: Co-author
 Name: Dr. Petr Císař Institution: Institute of signal and image
 processing, University of South Bohemia in České Budějovice, Zámek 136,
 373 33, Nové Hrady, Czech Republic Email: 
 Co-author Name: MSc. Mohammadmehdi Ziaei Institution: Institute of signal
 and image processing, University of South Bohemia in České Budějovice,
 Zámek 136, 373 33, Nové Hrady, Czech Republic Email:
 Co-author Name: Prof. Dalibor Štys Institution:
 Institute of Complex Systems, University of South Bohemia in České
 Budějovice, Zámek 136, 373 33, Nové Hrady, Czech Republic Email:
 3. Date of data collection: 2022 4. Geographic
 location of data collection: Nové Hrady, Czech Republic 5. Funding sources
 that supported the collection of the data: Ministry of Education, Youth
 and Sports of the Czech Republic – project CENAKVA (LM2018099), European
 Regional Development Fund in the frame of the project ImageHeadstart
 (ATCZ215) in the Interreg V-A Austria–Czech Republic programme, and GAJU
 114/2022/Z. 6. Recommended citation for this dataset: Ghaznavi A.,
 Rychtáriková R., Císař P., Ziaei M., Štys D. Telecentric wide-field
 reflected light microscopic dataset (2022), Dryad, Dataset. DATA &
 FILE OVERVIEW 1- Description of HeLa dataset Human Negroid cervical
 epithelioid carcinoma line HeLa was chosen as a testing cell line for
 microscopy image segmentation task. The reason for choosing is that HeLa
 is the oldest, immortal, and most used model cell line ever. HeLa is
 cultivated in almost all tissue and cell laboratories worldwide and
 utilized in many fields of medical research, such as research on carcinoma
 or testing the material biocompatibility. Human HeLa cell line (European
 Collection of Cell Cultures, Cat. No. 93021013) was cultivated to low
 optical density overnight at 37°C, 5% CO2, and 90% relative humidity. The
 nutrient solution consisted of Dulbecco’s modified Eagle medium (87.7%)
 with high glucose (1 g L−1), fetal bovine serum (10%), antibiotics and
 antimycotics (1%), L-glutamine (1%), and gentamicin (0.3%; all purchased
 from Biowest, Nuaille, France). The HeLa cells were maintained in a Petri
 dish with a cover glass bottom and lid at temperature of 37°C. 2-
 Description of wide-field microscope Time-lapse image series of living
 human HeLa cells on the glass Petri dish were captured using a
 high-resolved wide-field light microscope (designed by the Institute of
 Complex System Nové Hrady, CZ; built by Optax, Prague, CZ and ImageCode,
 Brloh, CZ in 2021). The microscope has a simple construction of the
 optical path. The sample is illuminated by a Schott VisiLED S80-25 LED
 Brightfield Ringlight. The light reflected from a sample goes through a
 telecentric measurement objective TO4.5/43.4-48-F-WN (Vision & Control
 GmbH, Shul, Germany) to an Arducam AR1820HS 1/2.3-inch 10-bit RGB camera
 with a chip of 4912 × 3684 pixel resolution All these experiments were
 performed in time-lapse to observe cells’ behaviour over time. 3- Data
 preparation steps 3-1 Calibration of all time-lapse experiments images to
 avoid image background inhomogeneities and noise (more information in
 section 2-2 of DOI:). 3-2
 Conversion of the raw image representations to 8-bit colour (rgb) images
 of resolution (number of pixels) quarter of the original raw images by
 applying quadruplets of Bayer mask pixels. 3-3 Applying means denoising
 method to minimize the background noise in the constructed RGB images at
 preserving the texture details. 3-4 Cropping the image series to the 1024
 × 1024 pixel size. The steps described 3-1 -- 3-4 gave us 650 images from
 different time-lapse experiments on HeLa cells. 3-5 Multi-class manual
 labelling the cells in the images in apper platform as Ground-Truth (GT)
 multi class masks with the dimension of 1024 × 1024. 3-6 Usage of 650
 labelled images as training (80%), testing (20%), and evaluation (20% of
 the training set) sets for the different U-Net base networks. 4-
 METHODOLOGICAL INFORMATION The U-Net is a semantic segmentation method
 proposed on the FCN architecture. The FCN consists of a typical
 encoder–decoder convolutional network. This architecture includes several
 feature channels to combine shallow and deep features. The first
 architecture we applied to our dataset was a simple U-Net. We reached
 multi-class segmentation Mean-IoU score of 0.7062 for our dataset. In the
 second step, we applied VGG19-U-Net architecture to improve multi class
 segmentation result and achieved Mean-IoU score for our datasets reached
 to 0.7178. In the third step, we applied Inception-U-Net architecture to
 to classify the cells more accurately into right classes and achieved
 Mean-IoU score for our datasets reached to 0.7907. In the final step, we
 applied a ResNet34-U-Net architecture to overcome the gradient vanishing
 problem and achieve the most accurate categorical segmentation result. We
 improved the segmentation result slightly to Mean-IoU score of 0.8067.
 (More information in section 2-3 of DOI:
 .) 5- Training Models 5-1 The
 computation was implemented in Python 3.9. The framework for deep learning
 was Keras, and the backend was Tensorflow. 5-2 The whole method, including
 the Deep Learning framework, was transferred and executed on the Google
 Colab Pro + account with P100 and T4 GPU, 24 Gb of RAM, and 2 vCPU. 5-3
 The primary dataset (of 650 images) 5-4 Since the network architectures
 work with a specific size of the input images, all datasets were resized
 to the 512 × 512 pixel size. DATA-SPECIFIC INFORMATION FOR: Folder x_train
 The train folder contains images selected randomly among all datasets to
 train the models based on our Train set. DATA-SPECIFIC INFORMATION FOR:
 Folder y_train The train folder contains masks created manually as the
 Ground-Truth set (described in section 3-5) to train the models based on
 our Train set. DATA-SPECIFIC INFORMATION FOR: Folder x_validate The val
 folder contains images selected randomly among all datasets to train and
 validate the models using our Train set and Validation set. DATA-SPECIFIC
 INFORMATION FOR: Folder y_validate The val folder contains masks created
 manually as the Ground-Truth set (described in section 3-5) to train and
 validate the models using our Train set and Validation Set. DATA-SPECIFIC
 INFORMATION FOR: Folder x_test The test folder contains images selected
 randomly among all datasets to test the models' performance after the
 training process and evaluate the models' performance using
 appropriate metrics. DATA-SPECIFIC INFORMATION FOR: Folder y_test The test
 folder contains masks created manually as the Ground-Truth set (described
 in section 3-5) to test the models' performance after the training
 process and evaluate the models' performance with the Test set using
 appropriate metrics. 6- Evaluation metrics (More information section 2-5
 DOI:) Overall pixel accuracy
 (Acc): represents a per cent of image pixels belonging to the correctly
 segmented cells Precision (Pre): is a proportion of the cell pixels in the
 segmentation results that match the GT The Recall (Recl): represents the
 proportion of cell pixels in the GT correctly identified through the
 segmentation process F1-score or Dice similarity coefficient (m-Dice):
 states how the predicted segmented region matches the GT in location and
 level of details and considers each class’s false alarm and missed value
 Jaccard similarity index or Intersection over Union (m-IoU): is a
 correlation among the prediction and GT, and represents the overlap and
 union area ratio for the predicted and GT segmentation. 7- Result (More
 information section 3 DOI:)
 Simple U-Net: Acc = 0.9869 -- m-IoU = 0.7062 -- m-Dice = 0.8104
 Vgg19-U-Net: Acc = 0.9865 -- m-IoU = 0.7178 -- m-Dice = 0.8218
 Inception-U-Net: Acc = 0.9904 -- m-IoU = 0.7907 -- m-Dice = 0.8762
 ResNet34-U-Net: Acc = 0.9909 -- m-IoU = 0.8067 -- m-Dice = 0.8873