Summary of resolution performance of image plane digital holographic methods.
Abstract
We summarize a study involving simultaneous imaging of cervical cells from Pap-smear samples using bright-field and quantitative phase microscopy. The optimization approach to phase reconstruction used in our study enables full diffraction limited performance from single-shot holograms and is thus suitable for reducing cost of a quantitative phase microscope system. Over 48000 cervical cells from patient samples obtained from three clinical sites have been imaged in this study. The clinical sites used different sample preparation methodologies and the subjects represented a range of age groups and geographical diversity. Visual examination of quantitative phase images of cervical cell nuclei show distinct morphological features that we believe have not appeared in the prior literature. A PCA based analysis of numerical parameters derived from the bright-field and quantitative phase images of the cervical cells shows good separation of superficial, intermediate and abnormal cells. The distribution of phase based parameters of normal cells is also shown to be highly overlapping among different patients from the same clinical site, patients across different clinical sites and for two age groups (below and above 30 years), thus suggesting robustness and possibility of standardization of quantitative phase as an imaging modality for cell classification in future clinical usage.
Keywords
- cervical cell imaging
- quantitative phase
- cell classification and characterization
- early cancer diagnosis
1. Introduction
Cervical cancer is the fourth most prevalent cancer among women worldwide. Human Papillomavirus (HPV) is known to be the main cause of cervical cancer [1]. It is well-known that cervical cancer has long latency period. Pre-malignant abnormalities in cervical cells can take up to a decade to progress to carcinoma. Early diagnosis of pre-cancerous cervical cells and treatment help in halting the progression of this fatal cancer [2]. The five year survival rate for patients suffering from cervical cancer has been documented to be over 60% [3]. In spite of the long latency period for cervical cancer to develop, the mortality rate among cervical cancer patients is high in developing countries due to shortage of skilled clinicians and lack of effective screening tools [4, 5].
The cervix which is the innermost part of uterus is sub-divided between the endo-cervix and ecto-cervix regions. The endo-cervix is composed of glandular cells whereas the ecto-cervix is made up of squamous cells. Transformation zone is the place where these regions adjoin, and where most of the cervical cancer is known to originate [6]. Starting with the transformation zone the cells in the squamous region are typically classified as basal, para-basal, intermediate and superficial respectively. The majority of cells in a typical Pap-smear cell sample used for examination by clinicians are from the intermediate and superficial outer layers. The classification of precancerous cells as low grade and high grade is established through Bethesda system [7, 8] which is based on morphological changes in the cells (particularly the cell nuclei). Traditionally the detection of precancerous cervical cells is primarily performed using cytological screening. The widespread usage of liquid based cytology (LBC) in recent years has made the process of sample preparation and examination more uniform. However, screening methods based on visual inspection can suffer from both inter- and intra-observer variability. Machine learning based approaches have gained attention in this regard [9, 10, 11, 12, 13] and standard benchmark datasets of cervical cell images have been created [14, 15]. The goal of machine learning approaches is to make the process of cell classification at least semi-automated and to provide an assisting tool for cyto-pathologists.
The machine learning based studies have focused mainly on the 2D bright-field images of the cervical cells and their nuclei. Since cells are 3D objects, we believe that additional morphological information in the third (depth) dimension of cells, if available, can provide new information and help any image based cell classification. Digital Holographic Microscopy (DHM) is an interferometric imaging technology [16, 17, 18] which can fill this gap and provide quantitative phase information that may then be related to the depth dimension of the cells. When a coherent beam of light of wavelength
Here n(x,y,z) represents the refractive index distribution within the cell relative to its surroundings. It is important to understand that phase provides new non-redundant information that cannot be derived from the usual 2D bright-field images. The phase change
Traditional DHM systems are based on single-shot off-axis interference configuration or the multi-shot phase shifting con-figuration. The single-shot off-axis systems are simpler and cheaper to build but the conventional Fourier filtering approach for phase reconstruction in these systems leads to sub-optimal phase resolution. The multi-shot phase shifting configurations offer full resolution but are hardware intensive and require stringent vibration isolation thus making them difficult to employ in clinical settings. In recent years our group has developed optimization based phase reconstruction algorithms [24, 25, 26, 27] for single-shot DHM systems which offer the simplicity of hardware without compromising on resolution and quantitative phase accuracy. The full diffraction-limited resolution capability of our system allows us to treat the bright-field and phase images on par (with respect to their lateral resolution). The single-shot operation also reduces the cost of building a DHM system making it more accessible for wider deployment. Based on our imaging study we find that the phase images contain important morphological information associated with different classes of normal as well as abnormal cells. Further this phase information is seen to be robust across the samples from three clinical sites. Also the samples consisted of age group of 17–60 years of the subjects. The cell morphology captured as numerical parameters from the phase images can provide valuable additional information to clinicians over what they usually access with routine bright-field microscopy. Our results suggest that phase imaging can become an important clinical modality, and it should be possible to design phase-based software tools for clinicians to make better informed decisions with this new information. The Chapter is organized as follows. In Section 2 we explain the technique of digital holographic microscopy (DHM) and the nature of quantitative phase images along with our phase reconstruction methodology. Section 3 briefly describes the details of the cell samples used. The results are discussed in Section 4. In Section 4.1 we start by showing images of cervical cells in both bright-field and phase modalities to illustrate morphological changes in cervical cell nuclei. This is followed by PCA analysis of the cell data based on the morphological parameters derived from the cell images in Section 4.2. In Section 4.3 we describe our analysis to understand if the most important phase parameters for normal cells are consistent across different patients from same clinical site, across different clinical sites and between different age groups. Finally in Section 5 we provide concluding remarks.
2. Digital holographic microscopy (DHM)
DHM is an interferometric modality where the recorded image data
Here
Reconstruction of single-shot holograms is traditionally performed using the Fourier transform method. However, due to the low-pass filtering nature of this method image plane phase recoveries with full pixel resolution cannot be obtained using this approach. This poses a problem as the bright-field images available will then seem to have higher resolution even though both have been recorded using the same microscope objective. In order to have both bright-field and phase images with same diffraction-limited resolution, we reconstruct of the complex object wave
Here
The tuning parameter
and
It is important to note that the optimization procedure operates fully in the image domain making it possible to employ it over a region of interest and thus allowing full resolution reconstruction in near real time. In our study, a
Processing method | Single/Multi-shot | Resolution |
---|---|---|
Fourier filtering | Single-shot | Low resolution |
Phase shifting | Multi-shot | Full diffraction-limited |
Optimization | Single-shot | Full diffraction-limited |
The phase map
Since the arctangent function is defined only over the range
Modality | Parameter | Parameter descriptiona |
---|---|---|
Area | ||
Nucleus perimeter | Number of boundary pixels of M | |
Mean intensity of | R, G, B componentsAverage R, G, B values computed over mask | |
Variance of | R, G, B componentsVariance of R, G, B values computed over mask | |
N/C Ratio | Ratio of areas (Nucleus)/(Cytoplasm) Labels assigned: low =1, mid =2, high = 3 | |
Mean and maximum phase | ||
Optical volume | (Area) | |
Variance of phase | Variance | |
Roughness at scales | 1, 0.75, 0.5, 0.25 | |
Moment of inertia | of nucleus | |
Shift between geometric and phase centroid |
3. Details of samples
We imaged a total of 48,006 cervical cells from 291 Pap-smear slides from three different hospitals in Delhi: AIIMS (All India Institute of Medical Sciences, New Delhi), LHMC (Lady Hardinge Medical College, New Delhi) and MAMC (Maulana Azad Medical College, New Delhi) (see Table 3). The samples were upto three years old (not from current patients) and stored in the repositories at the respective sites. They were collected by following the standard protocols for the Pap-smear examination. The patients varied in age from 16–70 years and came from varied geographical locations in India. The cell samples can be prepared conventionally or with Liquid-based cytology (LBC). For each method staining is performed with Pap stain for visualization with bright-field microscopy. For our study we have used both types of samples. Two of the sites used liquid based cytology (LBC) slides prepared via ThinPrep and SurePath systems; while third site used conventional Pap-smears. In LBC method, samples are collected in liquid vials and the slide is prepared semi-automatically. The advantage of LBC is uniformity in sample preparation. On the other hand in conventional cytology the sample is applied directly to a slide for microscopic investigation. Table 3 provides details about number of cells imaged. The classification of normal vs. abnormal cells in these samples was provided based on the bright-field images by practicing cyto-pathologists (S. R. M., M. S.,K. A., S.S.). The normal cells here include the superficial and intermediate cells while the abnormal cells include LSIL, HSIL, SCC, ASC-H and ASC-US type of cells [31].
Clinical site | Preparation method | Number of patients normal / with condition a | Normal cells | Abnormal cells |
---|---|---|---|---|
AIIMS, New Delhi | Liquid based cytology | 66 / 33 | 18801 | 532 |
(ThinPrep) | ||||
LHMC, New Delhi | Liquid based cytology | 174 / 9 | 28007 | 214 |
(SurePath) | ||||
MAMC, New Delhi | Conventional | 6/ 3 | 438 | 14 |
4. Results
4.1 Illustrative bright-field and phase images of various cervical cell types
In this section we begin by providing sample images of cervical cell nuclei that were obtained using our dual-mode DHM system. While quantitative information obtained in terms of morphological parameters is certainly important, a large number of clinical sites worldwide typically use visual examination of cells using a bright-field microscope for cell classification. The importance of changes in nucleus structure in cancer diagnosis is already well-known [32]. With the illustrative examples in this section, we wish to qualitatively describe the morphological features observed in quantitative phase images of cervical cell nuclei in various stages. The simultaneous presentation of bright-field images (that pathologists can correlate to) and the quantitative phase images as shown here is important in our opinion from the perspective of clinical users. It is important to note that our single-shot full resolution phase reconstruction technique allows us to observe the quantitative phase images with the same resolution as the bright-field images. The examples shown here also aim to illustrate that the information contained in the quantitative phase images is different in nature from that in the bright-field images. Quantitative analysis of the images using morphological parameters as described in Table 2 will be provided in the following sections. In Figure 2 we show illustrative examples of normal cells in the intraepithelial squamous layer. A progression from intermediate to superficial stages is shown in Figure 2(a)–(j) respectively. As a cell progresses from intermediate to superficial stage the chromatin in the cell nucleus is known to condense. The superficial cells are in the outermost layer of ecto-cervix and have highly condensed pyknotic nucleus. While in the bright-field images the area of the nucleus is progressively decreasing and the color of the nucleus gets darker from intermediate to superficial stages, the accompanying phase map of the nucleus is seen to get taller by approximately a factor of 2. This change in the optical height profile cannot be inferred from the 2D bright-field images and the phase map is therefore seen to provide new morphological information. Further, from the phase profiles we also see that the evolution of the cells from intermediate to superficial stage happens via a continuous change. Next in Figures 3–5 we examine the abnormal cell classes low grade squamous intraepithelial lesion (LSIL), high grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma (SCC) which progressively indicate higher grade abnormalities. The class LSIL consists of abnormal superficial and intermediate cells. Variable degrees of hyper-chromasia, nuclear size variation with coarsely granulated chromatin are identifiers of a typical LSIL cell. In the HSIL class, the degree of nuclear enlargement and hyper-chromasia is more than LSIL and the cells here are found in sheet-like aggregates. In both LSIL and HSIL cases the phase profiles of the cell nuclei are seen to have increased roughness or corrugations compared to the normal cells. In the SCC class which is considered to be a confirmed case of malignant cervical smear, the phase profile of the nucleus shows sharp narrow peaks with large phase values. It is once again important to note that the phase profile clearly provides new morphological information that is not readily available in the 2D bright-field images. Apart from the main classes above, the Bethesda system defines a class Atypical Squamous Cells ASC which include the samples that cannot be categorized as normal or abnormal typically. The nuclei in this class are typically larger in area, however, as seen in Figure 6, the average phase value in the nucleus is slightly lower compared to the LSIL, HSIL or SCC classes. In both ASC-US and ASC-H classes (Figure 6), the phase profile has undulations but the phase structure of ASC-H is flatter with lower average phase value in the nucleus as compared to that of the ASC-US cells. Further in some ASC nuclei, we observe local peaks in phase profile located near the nuclear boundary leading to a dip in the center. Finally we show rare abnormal cell cases in Figure 7 that include inflamed, reactive, moon-type, virus-infected and koliocytotic classes. Just a few examples of these rare cells were present in our sample set. The phase profile for all these types appears corrugated with lower average phase values except for the virus infected cell type. While only a few representative images of each cell type have been shown simultaneously in bright-field and phase mode, we clearly observe that the phase profile offers distinctive morphological features that are not currently utilized in the clinical practice. This new information if incorporated in cyto-pathological examination, can be potentially valuable to clinicians.
4.2 PCA analysis of quantitative parameters obtained from bright-field and phase images of cervical cell nuclei
A MATLAB based software was designed to compute a number of morphological parameters associated with cell nuclei that are listed in Table 2 from each of the cell nuclei imaged in bright-field as well as quantitative phase mode. The cell nucleus measurement data was therefore consisted of an (N × p) matrix with N = 48,006 and p = 20. Note that one parameter in the measurement set is the N/C ratio which was given three labels (low =1, mid =2, high =3) based on visual inspection of nuclei. This was done so that a number of cells of interest that appeared in clusters for which determining the cytoplasm boundary was difficult could be used in the analysis. All the other parameters were measured over the nucleus region in an automated fashion. For the present analysis, the cells were nominally labeled as superficial, intermediate and abnormal (including LSIL, HSIL, ASC-US, ASC-H, SCC) by practicing cyto-pathologists (S. R. M., M. S., K. A., S.S.). Since the number of abnormal cells was much smaller (1.6%) compared to the normal (superficial and intermediate) cells, a truncated data-set with 450 randomly selected cells from each of the three types (superficial, intermediate and abnormal) as per prior labelling was used to train the PCA. Denoting the truncated data matrix with 1350 rows and 20 columns (representing the measurements) with each column in standard form (zero mean and standard deviation 1) by
Here the superscript “T” stands for the transpose of the matrix
We further performed a leave-one-out analysis of the PCA for the cell data to determine which of the 20 measurements influenced the PCA scores the most [33]. If the PCA eigenvectors are arranged as columns of a matrix
The plot in Figure 8 thus corresponds to the first two columns of the score matrix
Morphological parameter | Modality | Relative Procrustes Distance |
---|---|---|
Moment of inertia | Phase | 1.0 |
Optical volume | Phase | 0.85 |
N/C Ratio | Bright-field | 0.83 |
Perimeter of nucleus | Bright-field | 0.79 |
Mean phase of nucleus | Phase | 0.76 |
4.3 Consistency of phase parameters
Quantitative phase is not a standard clinical methodology for cell classification, however, as we showed in Section 4.2, quantitative phase may become an important modality to consider for future clinical use. It is therefore important to understand if the phase parameters for cervical nuclei are consistent across different subjects from same clinical site, age group of subjects or clinical sites with different sample preparation methodologies. Since our leave-one-out PCA analysis suggested that optical volume and moment of inertia are the most important phase parameters as explained in the previous section, we have plotted a few hundred randomly selected normal cells (superficial and intermediate) with respect to these phase parameters in Figure 10. In Figure 10(a) we show the plot for 200 normal cells each for five different patients from a single clinical site. Figure 10(b) shows the same plot for 200 cells each from three different clinical sites with different sample preparation protocols. In Figure 10(c) we show the plot once again for 500 normal cells for two different age groups (below and above 30 years). From these plots we observe that the normal cells from different categories as above show highly overlapping distributions for the most important phase parameters. We believe that this observation is very important for standardization and usage of quantitative phase imaging methodology in future clinical practice.
4.4 Observations on quantitative phase imaging of unstained cervical cells
In this section we briefly describe an interesting possibility of quantitative phase imaging of unstained cervical cell samples with two typical images of normal cells as shown in Figure 11. For unstained cervical cell samples, the Pap smear was prepared using the conventional method and cells were fixed with ethyl alcohol. While the staining protocols used in Pap-smear sample is a gold standard for diagnosis by cyto-pathologists, we note here that compared to stained cells, the phase signal observed from nuclei of unstained unprocessed cell samples is almost three times higher in magnitude. While interpretation of images of the unstained cells and their phase may require one to go through a learning process, the possibility of using unstained cell samples for diagnostic practice may offer an attractive alternative as the cell sample preparations will not involve any wet-lab processing and recurring costs associated with reagents.
5. Conclusions
In conclusion we have reported an image based study of cervical cells at various stages using bright-field as well as quantitative phase microscopy. Over 48000 cells have been imaged individually. The phase images of the cell nuclei were reconstructed using an optimization approach that provided same resolution as the bright-field images. This image data-set may be valuable for future application development using advance machine learning methods. The visual inspection of images shows interesting features in the phase images as the cells evolve from intermediate to superficial stages with distinct features associated with abnormal cells. This finding based on visual inspection is confirmed in the PCA analysis of the morphological parameters of cells derived from both the bright-field and phase images of cell nuclei. A leave-one-out analysis applied to the PCA scores suggests that apart from the N/C ratio that has been used for identifying abnormal cells for decades, the other two parameters that influence the PCA the most are optical volume and moment of inertia of nucleus - both of which are derived from phase images. A consistency study suggests that the phase parameters associated with normal cells show highly overlapping distributions for multiple patients from same clinical site, for three clinical sites with different sample preparation protocols and for patients in two age groups. The consistency of phase parameter distributions for these cases further suggest that phase is a robust modality that can certainly be used in a standardized manner in clinical practice. We believe that quantitative phase may become an important imaging modality in addition to the bright-field imaging that is solely used in the current clinical practice. While this study has been performed for cervical cells we believe that our conclusions regarding importance of quantitative phase may possibly have a wider applicability.
Acknowledgments
The authors acknowledge financial support from Department of Science and Technology India (Award: IDP/MED/34/2016) and Biotechnology Industry Research Assistance Council India (Awards: FT/12/02(110)/15/0237 and BT/SIBRI45/733/17). The unstained sample shown in Figure 11 of this chapter was shared by Dr. R. Sankaranarayanan (RTI International India). Incubation support from FITT-IIT Delhi is gratefully acknowledged.
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