Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE)

Sharif Chowdhury 0 Meenakshisundaram Kandhavelu 0 Olli Yli-Harja 0 1 Andre S Ribeiro 0 0 Laboratory of Biosystem Dynamics, Computational Systems Biology Research Group, Department of Signal Processing, Tampere University of Technology , 33101 Tampere , Finland 1 Institute for Systems Biology , 401 Terry Avenue North, Seattle, WA 98109-5234 , USA Background: Cell imaging is becoming an indispensable tool for cell and molecular biology research. However, most processes studied are stochastic in nature, and require the observation of many cells and events. Ideally, extraction of information from these images ought to rely on automatic methods. Here, we propose a novel segmentation method, MAMLE, for detecting cells within dense clusters. Methods: MAMLE executes cell segmentation in two stages. The first relies on state of the art filtering technique, edge detection in multi-resolution with morphological operator and threshold decomposition for adaptive thresholding. From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function. Also, it acquires morphological features from the initial segmentation for constructing the likelihood parameter, after which the final segmentation is obtained. Conclusions: We performed an empirical evaluation that includes sample images from different imaging modalities and diverse cell types. The new method attained very high (above 90%) cell segmentation accuracy in all cases. Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy. - From 10th International Workshop on Computational Systems Biology Tampere, Finland. 10-12 June 2013 Background Single cell microscopy and subsequent analysis has gained much interest recently in areas ranging from studies of gene expression dynamics [1-3], to studies of cell aging [4,5] to disease classification [6]. However, as most processes in cells are stochastic in nature [7] their study requires high-throughput measurements and analysis. The manual extraction of the results from the raw image data is thus prohibitive, causing a need for accurate and robust methods of cell segmentation. Most existing methods lack in generic applicability and require strong assumptions on cell features i.e. cell shape, size, etc. Additionally, their performance is highly sensitive to cell density and signal to noise ratio. One of the presently most successful and used cell image analysis tools is Cellprofiler [8], an open source software platform for automated cell segmentation from microscopy images. Cell segmentation in Cellprofiler is performed in two steps. First, it separates objects from image background by thresholding. Next, the clumped objects are segmented again by considering intensity or shape as a feature for discrimination. Cellprofiler provides several alternatives for automated threshold selection and clumped cell segmentation. The major drawback of its segmentation algorithm is that its accuracy decreases significantly when cells are in large, dense clumps. Another state of the art software tool is Schnitzcells [9]. Schnitzcells provides solutions for segmentation and tracking of Escherichia coli cells from images by confocal or phase contrast microscopy. The segmentation of cells in Schnitzcells is a multi-stepped process. First, it applies edge detection for generating initial segmentation. Next, it splits long or clumped cells. Finally, it considers too small objects as false positives and discards them. The major problem is the large number of parameters that, without proper tuning, cause the accuracy of the segmentation to decrease notably. Further, it has a limited scope of application, i.e. it only handles E. coli and Bacillus subtilis cells and often presents a significant number of false positives. Finally, it is worth mentioning the cell segmentation algorithm for histopathology images, whose implementation was made available in the Farsight toolkit [10]. This method exploits graph-cuts-based segmentation for segmenting foreground signals from the image background. Then, the nuclear seed points are detected by a multiresolution edge detection method. Aside these, other methods for cell segmentation were proposed (see e.g. [11,12]). In general, these split the overall segmentation task into three steps. First, a separation of foreground objects from image background is made. Next, a post processing step is applied to split the under-segmented clumped cells. Finally, false positives are discarded by some criteria. Here, we propose a novel cell segmentation method, MAMLE, which maintains very high cell segmentation accuracy in dense cell clusters with low signal to noise ratio (SNR). Moreover, MAMLE requires very few assumptions on cell shape or size, thus, it can handle a wide range of cell types in different imaging modalities. MAMLE is novel in that i) it adopts a state of the art image denoising technique for improving SNR in image, ii) unifies multiresolution edge detection and threshold decomposition to accomplish the initial segmentation iii) corrects the oversegmented and under-segmented cells based on likelihood estimate, which is shown to be adaptive to varying conditions. Above all, MAMLE innovates in that it learns different shape features on the fly and exploits the learnt parameters for cell segmentation correction. A properly combined usage of all features is implemented to obtain robust and accurate cell segmentation. MAMLE is primarily targeted towards one of the most challenging cell types for automated segmentation, E. coli, a model organism in cell and molecular biology research [13-15]. The high division rate, the formation of dense colonies and the cells morphology make the segmentation more challenging than for most other cell types. We first describe the method, after which we evaluate its cell segmentation accuracy and compare it with state of the art cell segmentation platforms. Next, the robustness of MAMLE is studied in its parametric space. In the end, we present our conclusions. Methods MAMLE cell segmentation method comprises 7 steps: i) image denoising, ii) foreground and background segmentation, iii) multi-scale morphological edge detection, iv) threshold decomposition and initial segmentation, v) shape learning form the initial segmentation, vi) likelihood optimization based splitting and vii) maximum likelihood based merging. A flow chart of the algorithm is illustrated in Figure 1. Next, we describe each step in detail: i) Image denoising: fluorescent images often have low SNR, which leads to cell detection artifacts. Hence, denoising filters are often applied as a pre-processing step for segmentation. MAMLE exploits a state of the art image denoising technique known as Block-Matching and 3D filtering (BM3D) [16]. We opted for BM3D due to its balance between noise cancellation and edge preservation capability [16]. BM3D (...truncated)