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Abstract
Real-time and long-term monitoring of the morphological changes of cells in biomedical science is highly desired. Quantitative phase imaging (QPI) obtained by various interferometric methods is the ideal tool for monitoring such processes as it allows to get quantitative information and thus assessing the right response on cell behaviors. Among QPI, digital holography (DH) in microscope configuration is a powerful tool as it is tolerant versus defocusing and for this reason is able to compensate for eventual defocusing effect during long time-lapse recording. Moreover, DH dynamic phase imaging for biological specimens has several advantages, namely non-invasive, label-free, and high-resolution. However, in DH, one of the main limitations is due to the need compensate aberrations due to the optical components in the object beam. In fact, the image of the object is inevitably embedded in aberrations due to the microscope objective (MO) and other optical components in the optical setup. Here, we propose an automatic and robust phase aberration compensation method based on a synthetic difference (SD) image process. The method is able to detect automatically object-free regions. From such regions, hologram’s aberrations can be accurately evaluated and cleaned up in the final QPI maps. Thanks to our method, temporal evolutions of cell morphological parameters were quantitatively analyzed, hence helping in studying the drug sensitivity of ovarian cancer cells. The experimental results demonstrated that the proposed method could robustly separate the object-free region from the distorted phase image and automatically compensate the total aberrations without any manual interventions, extra components, prior knowledge of the object, and optical setup.
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
Digital holography (DH) in microscope configuration is a well-known technique allowing simultaneously to measure the amplitude and phase information of the object in a single-shot. DH is a very useful tool in biological microscopy thanks to several intrinsic advantages, i.e. non-invasive, label-free, and high-resolution in quantitative phase imaging (QPI). In the past decades, the technique has been used to investigate the living cell, such as observing cell division [1], tracking cell migration [2,3], measuring the fluctuations of the cell membrane [4], tomography of RBC or cancer cells [5,6], cyto-toxicology due to the cell uptake of heavy metals or graphene nanoparticles [7,8] or for detecting the drug responses of cell [9]. Moreover, it is applied in the real-time investigation of biological cells under various special application conditions, e.g., underwater environments [10], micro-gravity environments [11,12], and flow shearing conditions [13].
In practice, the hologram records are not only the phase information of the object but also the phase aberrations caused by the optical elements in the setup. In particular, the microscope objective (MO) in the object beam path induces a large and undesired quadratic phase curvature, while higher-order aberrations can be induced by any of the microscope components. Therefore, these aberrations are needed to be compensated in order to get the real phase image of an object. In the past decades, many researchers have proposed various strategies to compensate the aberrations, which can be categorized into two groups, i.e. physical methods and numerical methods, respectively. For the physical compensation methods, the phase aberrations are eliminated by making the curvature of the object beam match the reference beam. This kind of method could be realized in several different ways, such as introducing the same curvature into the reference beam via a same microscope objective [14] or a focus tunable lens [15,16], eliminating the curvature of the object beam by utilizing a telecentric optical setup [17,18], and designing a common path self-referencing configuration by using a glass plate [19], a cube beam splitter [20,21] or a mirror [22]. However, these mentioned approaches would partially compensate the lower-order phase aberrations and lead to a more complex system. The numerical way could remove the phase aberrations without any additional elements and it has been studied a lot. The geometrical transformation method reduces the phase aberrations via shearing [23], folding [24], rotating [25] or geometrical transforming [26] the wavefront numerically. This method is very fast but would decrease the field of view by half. The phase aberrations can be extracted by recording a reference hologram without object [27] or employing an absolute calibrate method [28], but the phase aberration may drift with the changes of focus and other experimental conditions. Another approach assumes that for biological samples the thickness is very thin when compared to the wavefront curvature due to the aberrations [29]. Compensation of aberration by numerically modelling of the wavefront curvature was firstly introduced within the paper in Ref. [27]. From this paper emerged the more general concept of digital phase mask (DPM) method is very flexible to compensate the phase aberrations, where the phase aberrations are presented by a DPM that could be modeled not only with parabolic function, tilts and spherical surface, but also with standard polynomials, or Zernike polynomials. The key point of the method is to determine the model parameters that could describe the phase aberrations precisely. Spectral analysis [30,31] and principal component analysis [32] could construct DPM in an automatic way, but both fail to compensate cross- and high-order aberrations. Recently, Zhang et al., improved the performance of the principle component analysis technique in fitting the cross-terms by using multi-step fitting [33]. Nonlinear optimization [34–36] allows to construct DPM without any manual involvement but this method is sensitive to noise or needs to determine the regularization parameter by trial and error. The 1D or 2D least-square fitting [37–39] is widely used to estimate DPM, which could remove second-order or high-order aberrations and have strong resistance to noise. These fitting methods require separate object-free regions from the distorted phase image. Though the detection and separation of the background in the distorted phase image can be performed by manual operation, the automaticity is sacrificed in dynamic observations. Traditional image segmentation methods such as threshold-based and edge-based techniques [40] can separate background in a fully automatic way. However, due to the phase aberrations and phase wrapping, it becomes unreliable to separate the object-free regions from a distorted phase image by using the image segmentation method. The phase background may be retrieved based on image segmentation after implementing a least-squares fitting to correct a proportion of the phase aberrations [41], but double fitting is needed for correcting total aberrations. A deep learning background detection method is proposed to identify object-free regions in distorted phase image [42], which needs to collect a large amount of data and consumes time for model training.
In this paper, we propose a robust and automatic phase compensation method based on a synthetic difference (SD) image background detection. Based on a reconstructed complex wavefront, an SD image is generated by digitally synthesizing multiple difference images with different shift directions. Because SD images are free from phase curvature, they have high-contrast object edges and flat background. Thereby, the object-free regions can be identified and separated from the SD image. Subsequently, a DPM is obtained by performing a least-squares fitting within the object-free regions. Finally, the phase aberrations are compensated by subtracting the DPM from the distorted phase image. Our approach can robustly detect object-free regions and automatically compensate all phase aberrations without human intervention, prior knowledge of the object and optical setup. The experimental results demonstrate the advantages of the proposed approach by investigating the morphological changes of living ovarian cancer cells under drug treatment.
2. Principle of the method
Figure 1 shows a DH setup suited for biological specimen analyses. This setup is used in an afocal configuration, where the back focal plane of the microscope objective (MO) coincides with the front focal plane of the tube lens (TL), with the object placed at the front focal plane of the MO. A coherent light emits from a solid-state laser (532 nm, 100 mW, single-mode fiber output) and propagates through a collimating lens (L) to produce a plane wave. This wave is split into the illumination beam and the reference beam R(x, y) by a polarizing beam splitter (PBS). In the object arm, the object beam O(x, y) is formed after the illumination beam transmitting through the condenser lens (CL), the object, the MO (Olympus UPLFLN, 20X, NA=0.50), and the TL sequentially. A half-wave plate (HPW1) is placed in front of the PBS to adjust the intensity ratio between R(x, y) and O(x, y). Another half-wave plate (HPW2) is set in the reference arm to make the polarization direction of O(x, y) and R(x, y) consistent. A variable optical delay line (DL) in the reference arm makes it possible to match both arms of optical path precisely. Finally, R(x, y) is reflected by the beam splitter (BS) and combined with O(x, y) with a small tile angle. Thus, an off-axis hologram is generated and recorded by a charge-coupled device (CCD, 1024×1024 pixels, 5.86 µm, PointGrey, Canada). The lateral resolution of the system approximates to be 700 nm.
Fig. 1. Schematic setup of DHM. HWP1-HWP2: half-wave plate; M1-M7: mirrors; L: lens; PBS: polarizing beam splitter; BS: beam splitter; CL: condenser lens; MO: microscope objective; TL: tube lens.
After hologram recording, O(x, y) can be reconstructed by using digital holographic reconstruction algorithms. Due to MO, CL, TL, and other elements in the setup, the phase image of the object is embedded in the aberrations, so it can be described as
where Ψo(x, y) is the phase map of the object only. Ψa(x, y) is the phase aberrations, which could be modeled by the Zernike polynomials [37] and formulated as where Zi(x, y) is the i-order Zernike polynomial, and ci is the corresponding coefficient. The research results proved that [37], based on the phase data in object-free regions of the distorted phase image, ci can be acquired by performing a least-squares fitting procedure. Therefore, the key task is to separate the object and background from the distorted phase image. However, due to the phase aberrations and phase wrapping, it becomes difficult to accurately determine the object-free regions by using the threshold-based image segmentation method. Based on the reconstructed complex wave-front, a difference image (DI) can be obtained by numerically shearing and described as [43,44]To achieve high-contrast on all object edges and plat background in the whole image, a synthetic difference image (SDI) is obtained by synthesizing the difference images with different shear directions of θ. Considering the positive gradient at rising edges and negative gradient at falling edges, the modulus of all difference images are added in synthetic processing, So, the SDI is written as
Next, we will demonstrate the effect of shear direction, θ, and the shift distance, ρ, on the contrast of difference image. Moreover, the selection of the number of difference images and the shift distance are discussed to build an appropriate SDI. First, a digital hologram of the living cells, ovarian cancer cells, is recorded by the DH. Figure 2(a) shows the digital hologram, Fig. 2(b) is the reconstructed phase image without aberration compensation. Figure 2(c) is the unwrapped phase image and Fig. 2(d) presents the segmentation result using the Ostu threshold segmentation method [45]. It can be seen that, due to phase aberrations, part of the objects and part of the background region are misidentified.
Fig. 2. The segmentation result based on the distorted phase image. (a) a digital hologram of living cells; (b) the reconstructed phase image; (c) unwrapped phase image; (d) segmentation result by the Ostu threshold method. The scale bar indicated by the solid white line is 20 µm and the unit of the colorbar is radian.
In order to demonstrate the shadow-cast effects at different shear directions, based on the complex wavefront reconstructed from the hologram shown in Fig. 2(a), difference images and corresponding zoom images are displayed in Fig. 3 for different direction angles while the shear distance is kept at 10 pixels. In Figs. 3(a)–3(h), the direction angle of θ changes from 0° to 315° with an interval of 45°. The shadow-cast effects are evident and related to the shift direction, as shown in zoom images. Because of these shadow-cast effects, the boundaries between objects and background are more visible in difference image than in the phase image along the shear direction.
Fig. 3. Difference images with different shift directions and the same shift distance of ρ = 10 pixels. (a) θ=0°; (b) θ=45°; (c) θ=90°, (d) θ=135°; (e) θ=180°; (f) θ=225°, (g) θ=270°; (h) θ=315°. The scale bar indicated by the solid white line is 20 µm.
An important parameter for generating an appropriate SDI is the shift distance ρ. The difference images are displayed in Fig. 4 for different shift distances, while the contrast profiles along the dashed lines are shown in zoom images. In Figs. 4(a)–4(h), the shift distance is changed from 5 to 40 pixels with an interval of 5 pixels, and the direction angle is kept at 0°. It is evident that, along the shift direction, the contrast of boundaries between the object and the background is enhanced with an increasing number of the shift pixels. Nevertheless, a distortion happens at object edges. Specifically, the object edges are expanded as the shift distance increasing, as shown in zoom images. Therefore, a compromise between contrast and distortion is desirable. In particular, the shift distance of 10 pixels is the best choice.
Fig. 4. Difference images deduced from one hologram with different shear distances and the same shear direction of θ = 0°. (a) ρ = 5 pixels; (b) ρ = 10 pixels; (c) ρ = 15 pixels; (d) ρ = 20 pixels; (e) ρ = 25 pixels; (f) ρ = 30 pixels; (g) ρ = 35 pixels; (h) ρ = 40 pixels. The scale bar indicated by the solid white line is 20 µm.
Another parameter to be chosen is the number of difference images. SDIs generated with different numbers of difference image are displayed in Fig. 5 while the shift distance is fixed at 10 pixels. It is noted that when the number of difference images is n, the angular interval of shear direction is 360° divided by n. In Figs. 5(a)–5(d), SDIs are assembled with n=2, 4, 8, and 16, respectively. The corresponding time to generate these four SDIs from a raw hologram are 0.174s, 0.261s, 0.396s and 0.684s, using MATLAB R2019b on a 2.40 GHz laptop. It is clear that the contrast of boundaries between object and background is enhanced by increasing the number of difference images and the binary mask is more accurately built up by threshold-based image segmentation. However, the more calculating time the bigger number of difference images is used to add together, so there is a compromise between processing time and image contrast. It can be seen that when n is 8 the quality of SDI is enough for generating an appropriate binary mask.
Fig. 5. SDI generated with different numbers of difference images with different shift directions. The scale bar indicated by the solid white line is 20 µm.
Based on the optimal parameters ρ=10 pixels and n=8, an appropriate SDI is built up and then the aberration compensation is accomplished. Figure 6 is a clear example of the processing procedure of the proposed method. A distorted phase image is displayed in Fig. 6(a) and its unwrapped result is shown in Fig. 6(b). First, the SDI is generated by shearing the complex wave-front with the optimal parameters and shown in Fig. 6(c). Then, the binary mask marking object-free regions is acquired by implementing image segmenting, holes filling and morphological closing, and shown in Fig. 6(d). Subsequently, the DPM contained all aberrations is obtained by performing a least-squares fitting with phase data in the object-free regions and shown in Fig. 6(e). Finally, the aberration compensation is achieved by subtracting the DPM from the reconstructed phase image and the result is shown in Fig. 6(f). The 3D view of the aberration-free phase image is displayed in Fig. 6(h). Further, the profiles along the horizontal and vertical lines in Fig. 6(f) are displayed in Fig. 6(g), which are flat in the background region. It is demonstrated that all aberrations in the phase image have been totally corrected without any manual interventions, extra components, prior knowledge of the object, and optical setup.
Fig. 6. Phase aberration compensation by using the proposed method. (a) distorted phase image; (b) unwrapped phase image; (c) SDI with n = 8, ρ = 10 pixels; (d) binary mask marked the object-free regions; (e) digital phase mask; (f) phase image after aberrations compensation; (g) phase profiles along the lines shown in (f); (h) 3D rendering of (f). The scale bar indicated by the solid white line is 20 µm and the unit of the colorbar is radian.
3. Experiments and results of live cancer cells
The performance of the proposed method is validated by imaging the living cells with DH. First, the several types of living cells with different morphologies are observed, including cervical cancer, endometrial carcinoma, osteoblastic MLO-Y4, and human mesenchymal stem cells. The results are displayed in Fig. 7, where the first column presents the reconstructed phase images before aberration compensation, the second column displays the corresponding SDIs obtained with same setting of ρ = 10 pixels and n = 8, the third column presents the binary mask marked object-free region, and the fourth column displays the phase images after aberration compensation. It can be seen that although the irregular cell morphology and complex aberration pattern, the object and background can be accurately identified thanks to the distinguishing capable of SDIs. The experimental results demonstrate the robustness of the proposed method.
Fig. 7. Experimental results of living cells. The first column presents the distorted phase images of different cells. The second column presents corresponding SDIs; The third column presents the binary masks marked the object-free regions. The fourth column presents the aberrations-compensated phase images. The scale bar indicated by the solid white line is 20 µm and the unit of the colorbar is radian.
Second, the performance of the proposed approach in dynamic imaging is demonstrated by observing the morphology alteration of ovarian cancer cells exposed to cisplatin. Ovarian cancer is one of the seventh most common cancers affecting women worldwide [46]. Cisplatin-based chemotherapy is the first-line chemotherapy regimen for ovarian cancer treatment [47]. Although ovarian cancer is a chemotherapy-sensitive disease, the presence of primary resistance disease exists in 15% of all patients, and the development of cisplatin-resistant disease in recurrent ovarian cancer presents a major therapeutic challenge [48]. Furthermore, the biomarkers of cell morphological changes have recently gained increasing importance for accessing drug resistance and sensitivity [49]. Recently, the morphological changes of endometrial cancer cells were measured by using DH and used to distinguish between non-drug-resistant cells and drug-resistant cells [50]. Here we measure the living cisplatin-treated ovarian cancer dynamically and quantitatively by using DH and the proposed approach. Then, based on the aberration-free phase images, the cell morphological parameters are investigated at different time periods.
The ovarian cancer cell line A2780 is derived from the ATCC (American Type Culture Collection) that was preserved in the Obstetrics and Gynecology Laboratory of Peking University People's Hospital. Cancer cells were cultured and grown in Roswell Park Memorial Institute-1640 (RPMI-1640) medium (HyClone, South Logan, UT, USA) containing 10% fetal bovine serum (FBS; Gibco, Rockville, MD, USA), and maintained in a 37°C, 5% CO2 incubator. Cancer cells were seeded onto a 35 mm glass Willco dish at a density of 5 × 103 cells per plate. After 24 h, cancer cells were divided 2 groups. In one group, that is the drug-treated group, cisplatin (Sigma–Aldrich, Steinheim, Germany) was added to the medium at a final concentration of 100 µg/ml. In another group, that is the control group, media were replaced with fresh culture medium without cisplatin. After that, the two groups of cancer cells were placed on the two-dimensional translation stage of the DHM setup to perform a scanning observation. One hundred fields of view (FOVs) were recorded for each group of cancer cells. Two different FOVs were selected from each group of cancer cells and their results are displayed in Fig. 8. The first column is the distorted phase images. It is evident that the aberrations are varying in different FOVs due to scanning observation. The second column exhibits the corresponding SDIs, in which the background is flat and the cell edges are sharp. The binary masks are presented in the third column, which marks object-free regions. The fourth column shows the phase images after aberrations compensation. Compared to the phase images in the first column and the fourth column, it demonstrated that the phase aberrations can be totally compensated.
Fig. 8. Experimental results of ovarian cancer a2780 cells. The first column presents the distorted phase images of different FOVs. The second column presents corresponding SDIs. The third column presents the binary masks marked the object-free regions. The fourth column presents the aberrations-compensated phase images. The scale bar indicated by the solid white line is 20 µm and the unit of the colorbar is radian.
Next, 2 groups of cancer cells are observed at 0 min, 60 min, 120 min, and 180 min after cisplatin treatment. The results in 4 FOVs are presented in Fig. 9. To quantify the robustness of the method for the time-lapse imaging, we define the parameter DPMrms by calculating the root-mean-square of the obtained DPMs of the different holograms across time, which is mathematically written as:
Fig. 9. Morphology alternations of a2780 cells after cisplatin treatment. The first column is the phase images at t = 0 min. The second column is phase images at t = 60 min. The third column is phase images at t = 120 min. The fourth column is phase images at t = 180 min. The scale bar indicated by the solid white line is 20 µm and the unit of the colorbar is radian.
Fig. 10. The parameters of the selected cells, including cell A, B, C, and D. (a) cell project area (CPA); (b) cell volume (CV); (c) cell height (CH); (d) change ratio (CR).
4. Conclusion
We proposed an automatic and robust phase aberration compensation method of wavefront phase aberrations that allows to make reliable monitoring of biological processes. By combining Zernike polynomial fitting technique with a synthetic difference (SD) image for intrinsic detecting the object-free region, we have demonstrated automatic removal of aberrations. SD image is generated by digitally synthesizing multiple difference images along different shift directions. Due to being free from phase curvature, the SD image has high-contrast boundaries between object and background. Based on the SD image, the object-free regions can be accurately separated by the threshold-based image segmentation. All aberrations could be accurately compensated by performing a Zernike polynomial fitting within the background region. We adopted the proposed approach for quantitatively assessing the morphological changes of ovarian cancer cells during cisplatin treatment. Thus, the experimental results validate the robustness and effectiveness of the proposed approach and demonstrate that it could automatically compensate the phase aberrations without any manual interventions, extra components, prior knowledge of the object, and optical setup, making DH microscope a reliable tool for real-time living cells monitoring. It should be noted that separating object-free regions from the distorted phase image is the crucial step in the background fitting method, which has been demonstrated to be one of the most effective methods enabling to remove both low-order and high-order aberrations. The core contribution of the proposed method is to give a totally new strategy to implement this operation in automatic and very robust way. In particular, the experiments demonstrate the robustness of the proposed method in respect to the previous methods by investigating different kinds of cells and compensating different phase aberrations in a real and relevant research experiments about cancer.
Funding
National Natural Science Foundation of China (61775010); Natural Science Foundation of Beijing Municipality (7192104).
Disclosures
The authors declare that there are no conflicts of interest related to this article.
References
1. B. Kemper, A. Bauwens, A. Vollmer, S. Ketelhut, P. Langehanenberg, J. Müthing, H. Karch, and G. von Bally, “Label-free quantitative cell division monitoring of endothelial cells by digital holographic microscopy,” J. Biomed. Opt. 15(3), 036009 (2010). [CrossRef]
2. X. Yu, J. Hong, C. Liu, and M. K. Kim, “Review of digital holographic microscopy for three-dimensional profiling and tracking,” Opt. Eng. 53(11), 112306 (2014). [CrossRef]
3. P. Memmolo, A. Finizio, M. Paturzo, L. Miccio, and P. Ferraro, “Twin-beams digital holography for 3D tracking and quantitative phase-contrast microscopy in microfluidics,” Opt. Express 19(25), 25833–25842 (2011). [CrossRef]
4. B. Rappaz, A. Barbul, A. Hoffmann, D. Boss, R. Korenstein, C. Depeursinge, P. J. Magistretti, and P. Marquet, “Spatial analysis of erythrocyte membrane fluctuations by digital holographic microscopy,” Blood Cells, Mol., Dis. 42(3), 228–232 (2009). [CrossRef]
5. F. Merola, P. Memmolo, L. Miccio, R. Savoia, M. Mugnano, A. Fontana, G. D’Ippolito, A. Sardo, A. Iolascon, A. Gambale, and P. Ferraro, “Tomographic flow cytometry by digital holography,” Light: Sci. Appl. 6(4), e16241 (2017). [CrossRef]
6. M. M. Villone, P. Memmolo, F. Merola, M. Mugnano, L. Miccio, P. L. Maffettone, and P. Ferraro, “Full-angle tomographic phase microscopy of flowing quasi-spherical cells,” Lab Chip 18(1), 126–131 (2018). [CrossRef]
7. M. Martina, P. Memmolo, L. Miccio, S. Grilli, F. Merola, A. C. Barroso, A. Bramanti, E. Mazzon, and P. Ferraro, “In vitro cytotoxicity evaluation of cadmium by label-free holographic microscopy,” J. Biophotonics 11(12), e201800099 (2018). [CrossRef]
8. M. Mugnano, G. C. Lama, R. Castaldo, V. Marchesano, F. Merola, D. del Giudice, A. Calabuig, G. Gentile, V. Ambrogi, P. Cerruti, P. Memmolo, V. Pagliarulo, P. Ferraro, and S. Grilli, “Cellular Uptake of Mildly Oxidized Nano-Graphene for Drug-Delivery Applications,” ACS Appl. Nano Mater. 3(1), 428–439 (2020). [CrossRef]
9. J. Kühn, E. Shaffer, J. Mena, B. Breton, J. Parent, B. Rappaz, M. Chambon, Y. Emery, P. Magistretti, C. Depeursinge, P. Marquet, and G. Turcatti, “Label-free cytotoxicity screening assay by digital holographic microscopy,” Assay Drug Dev. Technol. 11(2), 101–107 (2013). [CrossRef]
10. M. Bedrossian, C. Lindensmith, and J. L. Nadeau, “Digital Holographic Microscopy, a Method for Detection of Microorganisms in Plume Samples from Enceladus and Other Icy Worlds,” Astrobiology 17(9), 913–925 (2017). [CrossRef]
11. J. Kühn, “Digital holographic microscopy real-time monitoring of cytoarchitectural alterations during simulated microgravity,” J. Biomed. Opt. 15(2), 026021 (2010). [CrossRef]
12. F. Pan, S. Liu, Z. Wang, P. Shang, and W. Xiao, “Digital holographic microscopy long-term and real-time monitoring of cell division and changes under simulated zero gravity,” Opt. Express 20(10), 11496–11505 (2012). [CrossRef]
13. C. Minetti, T. Podgorski, G. Coupier, and F. Dubois, “Fully automated digital holographic processing for monitoring the dynamics of a vesicle suspension under shear flow,” Biomed. Opt. Express 5(5), 1554–1568 (2014). [CrossRef]
14. C. J. Mann, L. Yu, C. Lo, and M. K. Kim, “High-resolution quantitative phase-contrast microscopy by digital holography,” Opt. Express 13(22), 8693–8698 (2005). [CrossRef]
15. A. N. A. D. Oblas, D. I. H. I. Uluaga, G. E. S. Aavedra, M. A. M. A. Orral, and J. O. G. A. Ucerquia, “Physical compensation of phase curvature in digital holographic microscopy by use of programmable liquid lens,” Appl. Opt. 54(16), 5229–5233 (2015). [CrossRef]
16. D. Deng, J. Peng, W. Qu, Y. Wu, X. Liu, W. He, and X. Peng, “Simple and flexible phase compensation for digital holographic microscopy with electrically tunable lens,” Appl. Opt. 56(21), 6007–6014 (2017). [CrossRef]
17. E. Sánchez-ortiga, P. Ferraro, M. Martínez-corral, G. Saavedra, and A. Doblas, “Digital holographic microscopy with pure-optical spherical phase compensation,” J. Opt. Soc. Am. A 28(7), 1410–1417 (2011). [CrossRef]
18. A. Doblas, E. Sánchez-ortiga, M. Martínez-corral, and J. Garcia-sucerquia, “Accurate single-shot quantitative phase imaging of biological specimens with telecentric digital holographic microscopy of biological specimens with telecentric digital holographic microscopy,” J. Biomed. Opt. 19(4), 046022 (2014). [CrossRef]
19. A. S. G. Singh, A. Anand, R. A. Leitgeb, and B. Javidi, “Lateral shearing digital holographic imaging of small biological specimens,” Opt. Express 20(21), 23617–23622 (2012). [CrossRef]
20. W. Qu, K. Bhattacharya, C. O. Choo, Y. Yu, and A. Asundi, “Transmission digital holographic microscopy based on a beam-splitter cube interferometer,” Appl. Opt. 48(15), 2778–2783 (2009). [CrossRef]
21. Q. Weijuan, Y. Yingjie, C. O. Choo, and A. Asundi, “Digital holographic microscopy with physical phase compensation,” Opt. Lett. 34(8), 1276–1278 (2009). [CrossRef]
22. V. Chhaniwal, A. S. G. Singh, R. A. Leitgeb, B. Javidi, and A. Anand, “Quantitative phase-contrast imaging with compact digital holographic microscope employing Lloyd’s mirror,” Opt. Lett. 37(24), 5127–5129 (2012). [CrossRef]
23. P. Ferraro, D. Alferi, S. De Nicola, L. De Petrocellis, A. Finizio, and G. Pierattini, “Quantitative phase-contrast microscopy by a lateral shear approach to digital holographic image reconstruction,” Opt. Lett. 31(10), 1405–1407 (2006). [CrossRef]
24. G. Coppola, G. Di Caprio, M. Gioffré, R. Puglisi, D. Balduzzi, A. Galli, L. Miccio, M. Paturzo, S. Grilli, A. Finizio, and P. Ferraro, “Digital self-referencing quantitative phase microscopy by wavefront folding in holographic image reconstruction,” Opt. Lett. 35(20), 3390–3392 (2010). [CrossRef]
25. G. Province and N. A. Polytechnic, “Off-axis tilt compensation in common-path digital holographic microscopy based on hologram rotation,” Opt. Lett. 42(24), 5282–5285 (2017). [CrossRef]
26. D. Deng, W. Qu, W. He, X. Liu, and X. Peng, “Phase aberration compensation for digital holographic microscopy based on geometrical transformations,” J. Opt. 21(8), 085702 (2019). [CrossRef]
27. P. Ferraro, S. De Nicola, A. Finizio, G. Coppola, S. Grilli, C. Magro, and G. Pierattini, “Compensation of the inherent wave front curvature in digital holographic coherent microscopy for quantitative phase-contrast imaging,” Appl. Opt. 42(11), 1938–1946 (2003). [CrossRef]
28. W. He, J. Dou, Z. Yang, Z. Liu, and Z. Liu, “Sequential shift absolute phase aberration calibration in digital holographic phase imaging based on chebyshev polynomials fitting,” IEEE Photonics J. 12(1), 1–11 (2020). [CrossRef]
29. L. Miccio, D. Alfieri, S. Grilli, P. Ferraro, A. Finizio, L. De Petrocellis, and S. D. Nicola, “Direct full compensation of the aberrations in quantitative phase microscopy of thin objects by a single digital hologram,” Appl. Phys. Lett. 90(4), 041104 (2007). [CrossRef]
30. J. Min, B. Yao, S. Ketelhut, C. Engwer, B. Greve, and B. Kemper, “Simple and fast spectral domain algorithm for quantitative phase imaging of living cells with digital holographic microscopy,” Opt. Lett. 42(2), 227–230 (2017). [CrossRef]
31. H. Cui, D. Wang, Y. Wang, J. Zhao, and Y. Zhang, “Phase aberration compensation by spectrum centering in digital holographic microscopy,” Opt. Commun. 284(18), 4152–4155 (2011). [CrossRef]
32. C. Zuo, Q. Chen, W. Qu, and A. Asundi, “Phase aberration compensation in digital holographic microscopy based on principal component analysis,” Opt. Lett. 38(10), 1724–1726 (2013). [CrossRef]
33. X. Zhang, J. Sun, Z. Zhang, Y. Fan, Q. Chen, and C. Zuo, “Multi-step phase aberration compensation method based on optimal principal component analysis and subsampling for digital holographic microscopy,” Appl. Opt. 58(2), 389–397 (2019). [CrossRef]
34. S. Liu, Q. Lian, Y. Qing, and Z. Xu, “Automatic phase aberration compensation for digital holographic microscopy based on phase variation minimization,” Opt. Lett. 43(8), 1870–1873 (2018). [CrossRef]
35. S. Liu, W. Xiao, and F. Pan, “Automatic compensation of phase aberrations in digital holographic microscopy for living cells investigation by using spectral energy analysis,” Opt. Laser Technol. 57, 169–174 (2014). [CrossRef]
36. Z. Ren, J. Zhao, and E. Y. Lam, “Automatic compensation of phase aberrations in digital holograic microsocpy based on spare optimization,” APL Photonics 4(11), 110808 (2019). [CrossRef]
37. T. Colomb, E. Cuche, F. Charrière, J. Kühn, N. Aspert, F. Montfort, P. Marquet, and C. Depeursinge, “Automatic procedure for aberration compensation in digital holographic microscopy and applications to specimen shape compensation,” Appl. Opt. 45(5), 851–863 (2006). [CrossRef]
38. J. Di, J. Zhao, W. Sun, H. Jiang, and X. Yan, “Phase aberration compensation of digital holographic microscopy based on least squares surface fitting,” Opt. Commun. 282(19), 3873–3877 (2009). [CrossRef]
39. X. Lai, S. Xiao, Y. Ge, K. Wei, and K. Wu, “Digital holographic phase imaging with aberrations totally compensated,” Biomed. Opt. Express 10(1), 283–292 (2019). [CrossRef]
40. W. Khan, “Image Segmentation Techniques: A Survey,” JOIG 1(4), 166–170 (2014). [CrossRef]
41. S. Liu, Q. Lian, and Z. Xu, “‘’ Phase aberration compensation for digital holographic microscopy based on double fitting and background segmentation,',” Opt. Laser Eng. 115, 238–242 (2019). [CrossRef]
42. T. Nguyen, V. Bui, V. Lam, C. Raub, L. C. H. Chang, and G. Nehmetallah, “Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection,” Opt. Express 25(13), 15043–15057 (2017). [CrossRef]
43. L. Miccio, A. Finizio, R. Puglisi, D. Balduzzi, A. Galli, and P. Ferraro, “Dynamic DIC by digital holography microscopy for enhancing phase-contrast visualization,” Biomed. Opt. Express 2(2), 331 (2011). [CrossRef]
44. C. Liu, Y. S. Bae, W. Yang, and D. Y. Kim, “All-in-one multifunctional optical microscope,” Opt. Eng. 47(8), 087001 (2008). [CrossRef]
45. H. J. Vala and A. Baxi, “A review on Otsu image segmentation algorithm,” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 2(2), 387–389 (2013).
46. R. Pokhriyal, R. Hariprasad, L. Kumar, and G. Hariprasad, “Chemotherapy Resistance in Advanced Ovarian Cancer Patients,” Biomarkers Cancer 11, 1179299X1986081 (2019). [CrossRef]
47. R. Angioli, I. Palaia, M. A. Zullo, L. Muzii, N. Manci, M. Calcagno, and P. B. Panici, “Diagnostic open laparoscopy in the management of advanced ovarian cancer,” Gynecol. Oncol. 100(3), 455–461 (2006). [CrossRef]
48. A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu, and M. J. Thun, “Cancer Statistics, 2009 both sexes female both sexes estimated deaths,” Ca-Cancer J. Clin. 59(4), 225–249 (2009). [CrossRef]
49. S. Sharma, C. Santiskulvong, L. A. Bentolila, J. Y. Rao, O. Dorigo, and J. K. Gimzewski, “Correlative nanomechanical profiling with super-resolution F-actin imaging reveals novel insights into mechanisms of cisplatin resistance in ovarian cancer cells,” Nanomedicine (N. Y., NY, U. S.) 8(5), 757–766 (2012). [CrossRef]
50. T. Yao, R. Cao, W. Xiao, F. Pan, and X. Li, “An optical study of drug resistance detection in endometrial cancer cells by dynamic and quantitative phase imaging,” J. Biophotonics 12(7), e201800443 (2019). [CrossRef]
51. S. A. Aldossary, “Review on pharmacology of cisplatin: Clinical use, toxicity and mechanism of resistance of cisplatin,” Biomed. Pharmacol. J. 12(1), 07–15 (2019). [CrossRef]
52. M. L. Nickerson, N. Witte, K. M. Im, S. Turan, C. Owens, K. Misner, S. X. Tsang, Z. Cai, S. Wu, M. Dean, J. C. Costello, and D. Theodorescu, “Molecular analysis of urothelial cancer cell lines for modeling tumor biology and drug response,” Oncogene 36(1), 35–46 (2017). [CrossRef]
53. A. Saraste and K. Pulkki, “Morphologic and biochemical hallmarks of apoptosis,” Cardiovasc. Res. 45(3), 528–537 (2000). [CrossRef]
