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科技英语阅读与写作题目： Cell Segmentation in Digital Holographic Images学院： 电子工程学院 专业： 信号与信息处理 姓名： 王 鹏 学号： 1602120898 Partner:姓名： 张润东 学号： 1601120188 CELL SEGMENTATION IN DIGITAL HOLOGRAPHIC IMAGESNoha El-Zehiry1, Oliver Hayden2 and Ali Kamen11Medical Imaging Technologies, Siemens Healthcare, Princeton, NJ, USA2 In-Vitro DX and Bioscience, Siemens Healthcare, Erlangen, GermanyABSTRACTDigital Holographic Microscopy (DHM) is becoming recently very popular for cell imaging. (1) The main advantage of digital holographic microscopy over classical microscopy techniques is that it does not only provide the projected image of the object but also provides three dimensional information of the objects optical thickness. DHM technology could be the core of a label-free imaging for hematology applications. In an ideal framework, a blood sample can be imaged using DHM, machine learning approaches can (be used for)张润东-1601120188be used for指“用于；用来做”，后接名词或动名词，如本例中的cell extraction和computing。类似的用法有be used to do the cell extraction, differentiation and consequently computing all the relevant blood statistics such as the Mean Corpuscular Volume (MCV), the Red Blood Cell (RBC) count, Red Blood Cell Distribution Width (RDW). The most vital component in such a framework is accurate extraction of the cells. (2) This paper presents a novel approach to cell segmentation in which a probabilistic boosting tree classifier is trained to detect the centers of the cells using Haar-Features. The detected cell centers are used to trigger a marker-controlled power watershed segmentation to compute the cell boundaries. Additionally, we present a comprehensive evaluation of segmentation methods for cell extraction in digital holographic images.1. INTRODUCTIONDigital Holographic Microscopy (DHM) has received a lot of attention recently in cell imaging 1, 2, 3. (3) DHM, if associated with proper image processing algorithms, can serve as the fundamental building block in a label free cell diagnostics workflow. In such a workflow, accurate cell extraction is a vital step to perform the analysis. Therefore, thorough investigation of cell segmentation in DHM images is a persistent necessity. Current DHM application studies such as 1, 2 use simple segmentation tools or utilize generic cell segmentation tools 4 (that)张润东-1601120188that引导限定性定语从句修饰generic cell segmentation tools are not designed for DHM and the accuracy of their performance for DHM was not assessed in the literature. One notable exception is the cell segmentation approach presented by Yi et al. in 3. The algorithm in 3 uses a sequence of morphological operations on the phase image to generate markers for maker-controlled watershed segmentation. The approach is relatively complicated and the main disadvantage is its sensitivity to the parameters of the morphological operators such as the size of the structure element used in every operation. We will overcome this problem. In this paper, we present a two-step segmentation approach: Cell detection to localize the centers of the cells and cell segmentation to delineate the boundaries of the cells. Qualitative and quantitative assessment of our segmentation method is presented. Moreover, we introduce a comparison to the state-of-the art cell segmentation methods 3 and 4.(4) The rest of the paper is organized as follows: Section 2 presents the details of our proposed segmentation methods, section 3 introduces the experimental results and the comparison to other segmentation methods, then a concluding discussion is provided in section4.Fig. 1. Pipeline of Cell Extraction Algorithm. Left: original image superimposed by the probability map of a pixel being a cell center. Middle: Result of aggregation of the probability map response and generating the internal markers. Left: Segmentation results.2. METHODSMarker controlled watershed has been used repeatedly for cell segmentation 4, 3, 5, 6. Robust marker generation is necessary to obtain quality segmentation results. Most of the previous segmentation methods 3, 4 use a sequence of morphological operations to identify the best possible set of markers. One key problem with such approaches is (that)张润东-1601120188that引导表语从句，作problem的表语 it requires the tuning of many parameters for the steps within the morphological operation chain. (5) It is less likely to have a single set of parameters that could work efficiently for all the images. On the other hand, tuning the parameters for each single image is completely impractical and negates the high throughput advantage associated with DHM technology. Motivated by the previous drawbacks, we present a new cell segmentation approach. The novelty of our approach is two-fold: First, robust marker generation (using)张润东-1601120188现在分词作定语，修饰robust marker generation，由于是对象主动发出的动作，所以用现在分词而不用过去分词 cell center detection. (6) Second, we use power watershed for the cell segmentation which has been proven more efficient than conventional watershed in general segmentation problems 7. Figure 1 shows the pipeline of our cell extraction approach. 2.1. Robust Marker Generation(7) We consider the marker generation problem as an object detection problem where we aim at finding the positions of cell centers. For this purpose, we use a machine learning based approach instead of morphological operations to minimize the sensitivity to parameters choice. In this context, we use Probabilistic Boosting Tree (PBT) learning framework 8. In the learning phase, the PBT constructs a tree (in which张润东-1601120188which引导定语从句，in是由于先行词tree，顺序语序为node in the tree) each node combines a set of weak classifiers into a strong classifier. In testing phase, the conditional probability is computed at each node and the probability is propagated to the source of the tree to provide the overall probability. In our training, we use the Haar features 9 to form the weak classifiers. (8) In testing, we compute the probability of each pixel in the image being a center of a cell, the probability map is thresholded to keep only the pixels that are more likely to be a cell center.2.2. Aggregation of the Detection ResponsesAfter computing the probability map and applying the threshold, we get a high response inside each cell. However, the response is not necessarily smooth and connected which may lead to false identification of one cell as multiple cells. Therefore, to aggregate these response, we apply a clustering step on the thresholded probability map. The clustering serves two purposes, first, it aggregated the cell responses in a single cell. Second, it provides a larger set of pixels to form as the internal marker for the segmentation. The pixels of each cluster are merged into a single connected component that serves as an internal marker for the cell segmentation step. The middleimage in Figure 1 shows a sample of the markers computed after clustering. These are used as internal cell markers, external cell markers highlighting the background are obtained by applying watershed transform on the internal markers.2.3. Cell Segmentation using Power WatershedThe power watershed segmentation reviewed in this section was presented by Couprie et al. in 7. The formulation is performed on a discrete graph. A graph g=V, consists of a set of vertices vV and a set of edges e VV. An edge incident to vertices vi and vj is denoted eij. In our formulation, each pixel is identified with a node, vi. A weighted graph is a graph in which every edge eij is assigned a weightwij.(9) The seeded segmentation energy in 7 was given aswhere xi and xj are the binary labels associated with vertices vi and vj, respectively. F and B represent the sets of foreground and background markers. (It was shown in 7 that)张润东-1601120188It is that表示强调 when p and q 1, this leads to a more general watershed, namely, power watershed that yielded better results. Cell segmentation could benefit for the improvements associated with power watershed. In Section 3, we will present the comparison between the segmentation results using power watershed and watershed that has been used in the vast literature for cell segmentation.Donor 1Donor 2Data Set 3Donor 4OverallTP97.4%97.8 %97.3%96.5%97.2 %FP6.8%4.3%3.7%5.7%5 %Table 1. Results of the detection of the cell center. TP and FP are the true positive and false positive rates, respectively.3. EXPERIMENTAL RESULTSIn addition to the novel segmentation approach, we consider the evaluation and comparison of segmentation methods for DHM as a major contribution of the paper. We chose a subset of segmentation methods that we consider representative of the current segmentation methods. Qualitative and quantitative comparison will be presented in this section.3.1.Data Description and Cell DetectionThe data set is acquired using the QMod Holographic and Fluorescence Microscope 10. The cells were illuminated with a light source of wavelength = 550nm and the magnification objective is 60x 1. The number of donors used in this study is 4. The number of images for each donor (varies from张润东-1601120188vary from表示“从到不等”，后面需要加to。在本例中，“6-9”中的“-”指代了“to”) 6-9 images with a total of 28 images. Each image contains multiple cells varying from 10-30 with a total of 615 cells. To evaluate the accuracy of the detection algorithm, we use n-fold leave one out cross validation. Specifically, we did 4 training/testing sets. For each set we leave out all the images associated with a given donor and train the classifier on the images of the rest of the donors. For testing, we test only on the unseen data from the left out donor. (10) We exclude all the cells that are touching the boundaries of the image to ensure that the evaluation is performed only on valid cell candidates.The probabilistic boosting tree outputs a probability map of each pixel (being)张润东-1601120188being独立主格结构作伴随状语 a cell center. We threshold the probability map at p = 0:75 to consider only the pixels that are high likely to be cells. (11) The threshold does not provide a single cell center but rather a cluster of points in the center of the cell as depicted in the first image of Figure 1. In this image, the probability map is superimposed on the original image with the lost probability in red and the highest probability in blue. Table 1 shows the (12) result for the detection system.3.2. Cell SegmentationWe chose a representative subset of cell segmentation methods to compare against M1 4 and M2 3. Moreover, to decouple the effect of each contribution (the machine learning detection component and the power watershed segmentation component), we present two novel methods M3 that benefits from detection component only and M4 that benefits from the power watershed segmentation component only. The descriptions of methods M1- M5 are summarized as follows:1.Cell Profiler 4 (M1): Cell Profiler (is considered as)张润东-1601120188be considered as意为“被看作是”，后接名词；而be considered通常后接形容词 one of the benchmarks incell segmentation and has been repeatedly used by other research groups toobtain the segmentation and provide analysis.2. Marker Controlled Watershed for DHM (M2) 3: Dedicated cell segmentation for DHM technology was only discussed in 3. The method uses a complicated workflow to generate the markers for the watershed segmentation. The workflow is summarized as follows: (1) Image is normalized, (2) Otsus threshold is applied to obtain Ibin, (3) Holes are filled using morphological construction to Ibin, (4) Gradient image Igrad is computed using Sobel operator. (5) Morphological opening is applied to Ibin to obtain Iopen. (6) Morphological erosion (13) is applied to Iopen to obtain Ierode. (7) Morphological reconstruction is applied with Iopen as the mask and Ierode as a marker to obtain Irec. (8) Compute Isub as I open - Irec. (9) Apply morphological dilation on Ierode to obtain Idilate (10) Obtain the internal markers by combining Isub and Idilate. (11) Apply watershed transform on the internal markers to generate the external markers. (12) Apply marker controlled watershed segmentation using the markers generated in (11). Most of the previous steps require tuning of structure element parameters which makes the workflow error prone. Although the workflow was carefully crafted with specific parameters provided by the authors in 3 to avoid merging touching cells or eliminating small components, a single set of parameters does not work well in all scenarios. We tested several combinations of parameters to try to achieve the best results for our data set. After several experiments, we figured that the parameters provided by the authors in 3 work best. (14) Hence, for the comparison, we use the parameter selection in 3. One can argue that the improvement over 3 is due to performing the segmentation using power watershed rather than the generation of the accurate markers. So, (15) it is worth clarifying that the improvement is due to a combination of both factors. To decouple the effect of each component. We also compare against two other workflows (M3 and M4).3. Machine Learning Marker Generation with Watershed (M3): In this method, we use the markers generated by the machine learning based cell center detection totrigger watershed segmentation.4. Morphological Marker Generation with Power Watershed (M4): In this method, we generate the markers using the morphological workflow (M2) and apply power watershed for the segmentation instead of watershed.5. Machine Learning Marker Generation with Power watershed (M5): This is the proposed workflow which takes advantage of robust marker generation as well as more accurate segmentation using power watershed.Figure 2 shows a sample of our results. The gallery depicts the results of the five algorithms for three different images from different donors. The third column shows that all the algorithms work comparably well if the cells are sparsely distributed in the image. This, however, is not very practical as it does not efficiently utilize the full field of view. In practice, the cells may be very close to each other. In such scenarios, most of the morphological based approaches would fail in accurately extracting the cells. In the yellow boxes, we show an example where two cells are touching. The algorithm in 4 merges the two cells in one entity. The carefully crafted morphological workflow in 3 managed to separate the two cells as shown in the second row and (16) so did all the algorithms we developed as shown in the third, fourth and fifth rows. When the cells slightly overlaps, it becomes more challenging and morphological based methods fail to accurately extract the cells as shown in the examples in the green boxes. On the other hand, M4 and M5 can successfully extract the cells due to the robustness of the cell center detection component. It is worth noting that high throughput systems require a full utilization of the field of view which results in multiple occurrences of such touching or slightly overlapping cells. A limitation of the current approach is that when the overlap between the cells is large, the boundaries cannot be delineated accurately as depicted in the blue boxes. However, even in such scenario, our algorithm is more accurate than 4 and 3 because it still identified two cells which is important for anaccurate cell count.Fig. 2. Sample of the segmentation results obtained using the five different algorithms.For quantitative assessment, we chose the most common metrics for segmentation evaluation, namely, the sensitivity, specificity, Dice and Jaccard similarity index defined as:where TP, FP, TN and FN refer to the true positives, false positives, true negatives and false negatives, respectively.Table 2 shows the results for the methods M1-M5. The quantitative assessment shows that each component we introduced (contributed to)张润东-1601120188contribute to指“有助于”，后接名词或动名词，介词to后不能跟动词原形 improving the segmentation results. However, (17) it is evident that the robust localization of the cells using the cell detection framework played a more crucial role. While the sensitivity improved by 10% when using our machine learning marker generation, it only improved 2% when using power watershed instead of watershed.M1M2M3M4M5Sensitivity88.7083.3792.9484.5894.83Specificity98.1898.5797.4498.9298.44Jaccard80.9577.5581.5380.0686.51Dice89.3288.8489.7488.8492.66Table 2. Quantitative Comparison between the different Segmentation Methods.4. CONCLUSION AND FUTURE WORKThe paper presented a novel approach for cell segmentation in digital holographic microscopic images. The robust marker generation presented in the paper outperforms the morphological workflow for marker generation that is commonly used to initiate watershed segmentation. We introduced the power watershed to the cell segmentation problem and presented quantitative evidence that it works better than watershed. The paper presented a quantitative comparison of the proposed approach to the common methods used for cell segmentation. One limitation of our approach and all the methods (M1-M5) is that it is not capable of properly segmenting overlapping cells. In the future, we plan to add post processing that identifies the overlapping cells and formulates a layered segmentation algorithm to separate properly the overlapping cells.一、 语法分析(1) The main advantage of digital holographic microscopy over classical microscopy techniques is that it does not only provide the projected image of the object but also provides three dimensional information of the objects optical thickne