The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Takeo Kanade - One of the best experts on this subject based on the ideXlab platform.
-
detect cells and cellular behaviors in Phase Contrast Microscopy images
Medical Image Recognition Segmentation and Parsing#R##N#Machine Learning and Multiple Object Approaches, 2016Co-Authors: Mei Chen, Takeo KanadeAbstract:This chapter focuses on image analysis and understanding of live cell populations in time lapse Phase Contrast Microscopy. The computer vision tasks involve cell segmentation and cell behavior understanding, including cell migration, division (mitosis), death (apoptosis), and differentiation. We will describe the problem definition for each topic, introduce the general schools of approaches that have been explored, discuss details of the state-of-the-art algorithms, and propose promising directions for future investigation.
-
cell segmentation in Phase Contrast Microscopy images via semi supervised classification over optics related features
Medical Image Analysis, 2013Co-Authors: Hang Su, Takeo KanadeAbstract:Abstract Phase-Contrast Microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded Phases. Despite many years of study, computer-aided Phase Contrast Microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by Phase Contrast optics. Addressing the unsolved challenges, the authors propose (1) a Phase Contrast Microscopy image restoration method that produces Phase retardation features, which are intrinsic features of Phase Contrast Microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of Phase Contrast Microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a Phase Contrast Microscopy image is represented by a linear combination of the bases, which we call Phase retardation features. Images are then partitioned into Phase-homogeneous atoms by clustering neighboring pixels with similar Phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the Phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches.
-
efficient Phase Contrast Microscopy restoration applied for muscle myotube detection
Medical Image Computing and Computer-Assisted Intervention, 2013Co-Authors: Seungil Huh, Mei Chen, Takeo KanadeAbstract:This paper proposes a new image restoration method for Phase Contrast Microscopy as a mean to enhance the quality of images prior to image analysis. Compared to state-of-the-art image restoration algorithms, our method has a more solid theoretical foundation and is orders of magnitude more efficient in computation. We validated the proposed method by applying it to automated muscle myotube detection, a challenging problem that has not been tackled without staining images. Results on 300 Phase Contrast Microscopy images from three different culture conditions demonstrate that the proposed restoration scheme improves myotube detection, and that our approach is far more computationally efficient than previous methods.
-
apoptosis detection for non adherent cells in time lapse Phase Contrast Microscopy
Medical Image Computing and Computer-Assisted Intervention, 2013Co-Authors: Seungil Huh, Takeo KanadeAbstract:This paper proposes a vision-based method for detecting apoptosis (programmed cell death), which is essential for non-perturbative monitoring of cell expansion. Our method targets non-adherent cells, which float or are suspended freely in the culture medium—in Contrast to adherent cells, which are attached to a petri dish. The method first detects cell regions and tracks them over time, resulting in the construction of cell tracklets. For each of the tracklets, visual properties of the cell are then examined to know whether and when the tracklet shows a transition from a live cell to a dead cell, in order to determine the occurrence and timing of a cell death event. For the validation, a transductive learning framework is adopted to utilize unlabeled data in addition to labeled data. Our method achieved promising performance in the experiments with hematopoietic stem cell (HSC) populations, which are currently in clinical use for rescuing hematopoietic function during bone marrow transplants.
-
apoptosis detection for adherent cell populations in time lapse Phase Contrast Microscopy images
Medical Image Computing and Computer-Assisted Intervention, 2012Co-Authors: Seungil Huh, Dai Fei Elmer Ker, Takeo KanadeAbstract:The detection of apoptosis, or programmed cell death, is important to understand the underlying mechanism of cell development. At present, apoptosis detection resorts to fluorescence or colorimetric assays, which may affect cell behavior and thus not allow long-term monitoring of intact cells. In this work, we present an image analysis method to detect apoptosis in time-lapse Phase-Contrast Microscopy, which is non-destructive imaging. The method first detects candidates for apoptotic cells based on the optical principle of Phase-Contrast Microscopy in connection with the properties of apoptotic cells. The temporal behavior of each candidate is then examined in its neighboring frames in order to determine if the candidate is indeed an apoptotic cell. When applied to three C2C12 myoblastic stem cell populations, which contain more than 1000 apoptosis, the method achieved around 90% accuracy in terms of average precision and recall.
Laura Waller - One of the best experts on this subject based on the ideXlab platform.
-
algorithmic self calibration for optimized 3d quantitative differential Phase Contrast Microscopy
Quantitative Phase Imaging VII, 2021Co-Authors: Ruiming Cao, Michael Kellman, David Ren, Regina Eckert, Laura WallerAbstract:3D differential Phase Contrast (3D DPC) Microscopy uses asymmetric illumination patterns and axial scanning to recover volumetric maps of refractive index. To avoid the expense of automated axial scanning, we demonstrate 3D DPC without a z-stage by hand spinning the microscope’s defocus knob to scan the object axially while updating illumination patterns on the LED-array microscope. We utilize an inverse problem optimization to retrieve the sample’s volumetric information with measurements from unknown axial positions by jointly solving for each measurement’s axial position. Finally, we explore how to optimize the LED-array illumination patterns for varying axial sampling rates.
-
3d differential Phase Contrast Microscopy with axial motion deblurring
Imaging and Applied Optics Congress (2020) paper CF4C.2, 2020Co-Authors: Ruiming Cao, Michael Kellman, David Ren, Laura WallerAbstract:We demonstrate 3D Phase imaging using asymmetric illumination patterns and defocused intensity measurements taken with continuous axial motion. The sample’s 3D refractive index is reconstructed with a motion-corrected transfer function.
-
3d differential Phase Contrast Microscopy
Biomedical Optics Express, 2016Co-Authors: Michael Chen, Lei Tian, Laura WallerAbstract:We demonstrate 3D Phase and absorption recovery from partially coherent intensity images captured with a programmable LED array source. Images are captured through-focus with four different illumination patterns. Using first Born and weak object approximations (WOA), a linear 3D differential Phase Contrast (DPC) model is derived. The partially coherent transfer functions relate the sample's complex refractive index distribution to intensity measurements at varying defocus. Volumetric reconstruction is achieved by a global FFT-based method, without an intermediate 2D Phase retrieval step. Because the illumination is spatially partially coherent, the transverse resolution of the reconstructed field achieves twice the NA of coherent systems and improved axial resolution.
-
3d differential Phase Contrast Microscopy
Proceedings of SPIE, 2016Co-Authors: Michael Chen, Lei Tian, Laura WallerAbstract:We demonstrate three-dimensional (3D) optical Phase and amplitude reconstruction based on coded source illumination using a programmable LED array. Multiple stacks of images along the optical axis are computed from recorded intensities captured by multiple images under off-axis illumination. Based on the first Born approximation, a linear differential Phase Contrast (DPC) model is built between 3D complex index of refraction and the intensity stacks. Therefore, 3D volume reconstruction can be achieved via a fast inversion method, without the intermediate 2D Phase retrieval step. Our system employs spatially partially coherent illumination, so the transverse resolution achieves twice the NA of coherent systems, while axial resolution is also improved 2× as compared to holographic imaging.
-
3d differential Phase Contrast Microscopy with computational illumination using an led array
Optics Letters, 2014Co-Authors: Lei Tian, Jingyan Wang, Laura WallerAbstract:We demonstrate 3D differential Phase-Contrast (DPC) Microscopy, based on computational illumination with a programmable LED array. By capturing intensity images with various illumination angles generated by sequentially patterning an LED array source, we digitally refocus images through various depths via light field processing. The intensity differences from images taken at complementary illumination angles are then used to generate DPC images, which are related to the gradient of Phase. The proposed method achieves 3D DPC with simple, inexpensive optics and no moving parts. We experimentally demonstrate our method by imaging a camel hair sample in 3D.
Mei Chen - One of the best experts on this subject based on the ideXlab platform.
-
Spatiotemporal Joint Mitosis Detection Using CNN-LSTM Network in Time-Lapse Phase Contrast Microscopy Images
IEEE Access, 2017Co-Authors: Yu-ting Su, Yao Lu, Mei ChenAbstract:We present an approach to jointly detect mitotic events spatially and temporally in time-lapse Phase Contrast Microscopy images. In particular, we combine a convolutional neural network (CNN) and a long short-term memory (LSTM) network to detect mitotic events in patch sequences. The CNN-LSTM network can be trained end-to-end to simultaneously learn convolutional features within each frame and temporal dynamics between frames, without hand-crafted visual or temporal feature design. Owing to the LSTM layer, this approach is able to detect mitotic events in patch sequences of variable length, as well as making use of longer context information among frames in the sequences. To the best of our knowledge, this is the first work to detect mitosis using deep learning in both spatial and temporal domains. Experiments have shown that the CNN-LSTM network can be trained efficiently, and we evaluate this design by applying the network to original raw Microscopy image sequences to locate mitotic events both spatially and temporally. The data with which we validate the proposed method include C3H10 mesenchymal and C2C12 myoblastic stem cell populations. Our approach achieved the F score of 98.72% on the C2C12 data set, and the F score of 96.5% on the C3H10 data set. The results on both data sets outperform the traditional graph modelbased approaches by a large margin, both in terms of detection accuracy and frame localization accuracy. Furthermore, we have developed a framework to aid humans in annotating mitosis with high efficiency and accuracy in raw Phase Contrast Microscopy images based on the joint detection results using the proposed method. Under this framework, expert level annotations can be obtained in raw Phase Contrast Microscopy image sequences, and the annotations have shown to further improve the training performance of the CNN-LSTM network.
-
detect cells and cellular behaviors in Phase Contrast Microscopy images
Medical Image Recognition Segmentation and Parsing#R##N#Machine Learning and Multiple Object Approaches, 2016Co-Authors: Mei Chen, Takeo KanadeAbstract:This chapter focuses on image analysis and understanding of live cell populations in time lapse Phase Contrast Microscopy. The computer vision tasks involve cell segmentation and cell behavior understanding, including cell migration, division (mitosis), death (apoptosis), and differentiation. We will describe the problem definition for each topic, introduce the general schools of approaches that have been explored, discuss details of the state-of-the-art algorithms, and propose promising directions for future investigation.
-
efficient Phase Contrast Microscopy restoration applied for muscle myotube detection
Medical Image Computing and Computer-Assisted Intervention, 2013Co-Authors: Seungil Huh, Mei Chen, Takeo KanadeAbstract:This paper proposes a new image restoration method for Phase Contrast Microscopy as a mean to enhance the quality of images prior to image analysis. Compared to state-of-the-art image restoration algorithms, our method has a more solid theoretical foundation and is orders of magnitude more efficient in computation. We validated the proposed method by applying it to automated muscle myotube detection, a challenging problem that has not been tackled without staining images. Results on 300 Phase Contrast Microscopy images from three different culture conditions demonstrate that the proposed restoration scheme improves myotube detection, and that our approach is far more computationally efficient than previous methods.
-
understanding the Phase Contrast optics to restore artifact free Microscopy images for segmentation
Medical Image Analysis, 2012Co-Authors: Takeo Kanade, Mei ChenAbstract:Phase Contrast, a noninvasive Microscopy imaging technique, is widely used to capture time-lapse images to monitor the behavior of transparent cells without staining or altering them. Due to the optical principle, Phase Contrast Microscopy images contain artifacts such as the halo and shade-off that hinder image segmentation, a critical step in automated Microscopy image analysis. Rather than treating Phase Contrast Microscopy images as general natural images and applying generic image processing techniques on them, we propose to study the optical properties of the Phase Contrast microscope to model its image formation process. The Phase Contrast imaging system can be approximated by a linear imaging model. Based on this model and input image properties, we formulate a regularized quadratic cost function to restore artifact-free Phase Contrast images that directly correspond to the specimen’s optical path length. With artifacts removed, high quality segmentation can be achieved by simply thresholding the restored images. The imaging model and restoration method are quantitatively evaluated on Microscopy image sequences with thousands of cells captured over several days. We also demonstrate that accurate restoration lays the foundation for high performance in cell detection and tracking.
-
Detection of mitosis within a stem cell population of high cell confluence in Phase-Contrast Microscopy images
CVPR 2011, 2011Co-Authors: Mei ChenAbstract:Computer vision analysis of cells in Phase-Contrast Microscopy images enables long-term continuous monitoring of live cells, which has not been feasible using the existing cellular staining methods due to the use of fluorescence reagents or fixatives. In cell culture analysis, accurate detection of mitosis, or cell division, is critical for quantitative study of cell proliferation. In this work, we present an approach that can detect mitosis within a cell population of high cell confluence, or high cell density, which has proven challenging because of the difficulty in separating individual cells. We first detect the candidates for birth events that are defined as the time and location at which mitosis is complete and two daughter cells first appear. Each candidate is then examined whether it is real or not after incorporating spatio-temporal information by tracking the candidate in the neighboring frames. For the examination, we design a probabilistic model named Two-Labeled Hidden Conditional Random Field (TL-HCRF) that can use the information on the timing of the candidate birth event in addition to the visual change of cells over time. Applied to two cell populations of high cell confluence, our method considerably outperforms previous methods. Comparisons with related statistical models also show the superiority of TL-HCRF on the proposed task.
Lei Tian - One of the best experts on this subject based on the ideXlab platform.
-
optimal illumination scheme for isotropic quantitative differential Phase Contrast Microscopy
Photonics Research, 2019Co-Authors: Yao Fan, Lei Tian, Jiasong Sun, Qian Chen, Xiangpeng Pan, Chao ZuoAbstract:Differential Phase Contrast Microscopy (DPC) provides high-resolution quantitative Phase distribution of thin transparent samples under multi-axis asymmetric illuminations. Typically, illumination in DPC microscopic systems is designed with two-axis half-circle amplitude patterns, which, however, result in a non-isotropic Phase Contrast transfer function (PTF). Efforts have been made to achieve isotropic DPC by replacing the conventional half-circle illumination aperture with radially asymmetric patterns with three-axis illumination or gradient amplitude patterns with two-axis illumination. Nevertheless, the underlying theoretical mechanism of isotropic PTF has not been explored, and thus, the optimal illumination scheme cannot be determined. Furthermore, the frequency responses of the PTFs under these engineered illuminations have not been fully optimized, leading to suboptimal Phase Contrast and signal-to-noise ratio for Phase reconstruction. In this paper, we provide a rigorous theoretical analysis about the necessary and sufficient conditions for DPC to achieve isotropic PTF. In addition, we derive the optimal illumination scheme to maximize the frequency response for both low and high frequencies (from 0 to 2NAobj) and meanwhile achieve perfectly isotropic PTF with only two-axis intensity measurements. We present the derivation, implementation, simulation, and experimental results demonstrating the superiority of our method over existing illumination schemes in both the Phase reconstruction accuracy and noise-robustness.
-
optimal illumination scheme for isotropic quantitative differential Phase Contrast Microscopy
arXiv: Optics, 2019Co-Authors: Yao Fan, Lei Tian, Jiasong Sun, Qian Chen, Xiangpeng Pan, Chao ZuoAbstract:Differential Phase Contrast Microscopy (DPC) provides high-resolution quantitative Phase distribution of thin transparent samples under multi-axis asymmetric illuminations. Typically, illumination in DPC microscopic systems is designed with 2-axis half-circle amplitude patterns, which, however, result in a non-isotropic Phase Contrast transfer function (PTF). Efforts have been made to achieve isotropic DPC by replacing the conventional half-circle illumination aperture with radially asymmetric patterns with 3-axis illumination or gradient amplitude patterns with 2-axis illumination. Nevertheless, these illumination apertures were empirically designed based on empirical criteria related to the shape of the PTF, leaving the underlying theoretical mechanisms unexplored. Furthermore, the frequency responses of the PTFs under these engineered illuminations have not been fully optimized, leading to suboptimal Phase Contrast and signal-to-noise ratio (SNR) for Phase reconstruction. In this Letter, we provide a rigorous theoretical analysis about the necessary and sufficient conditions for DPC to achieve perfectly isotropic PTF. In addition, we derive the optimal illumination scheme to maximize the frequency response for both low and high frequencies (from 0 to 2NAobj), and meanwhile achieve perfectly isotropic PTF with only 2-axis intensity measurements. We present the derivation, implementation, simulation and experimental results demonstrating the superiority of our method over state-of-the-arts in both Phase reconstruction accuracy and noise-robustness.
-
3d differential Phase Contrast Microscopy
Biomedical Optics Express, 2016Co-Authors: Michael Chen, Lei Tian, Laura WallerAbstract:We demonstrate 3D Phase and absorption recovery from partially coherent intensity images captured with a programmable LED array source. Images are captured through-focus with four different illumination patterns. Using first Born and weak object approximations (WOA), a linear 3D differential Phase Contrast (DPC) model is derived. The partially coherent transfer functions relate the sample's complex refractive index distribution to intensity measurements at varying defocus. Volumetric reconstruction is achieved by a global FFT-based method, without an intermediate 2D Phase retrieval step. Because the illumination is spatially partially coherent, the transverse resolution of the reconstructed field achieves twice the NA of coherent systems and improved axial resolution.
-
3d differential Phase Contrast Microscopy
Proceedings of SPIE, 2016Co-Authors: Michael Chen, Lei Tian, Laura WallerAbstract:We demonstrate three-dimensional (3D) optical Phase and amplitude reconstruction based on coded source illumination using a programmable LED array. Multiple stacks of images along the optical axis are computed from recorded intensities captured by multiple images under off-axis illumination. Based on the first Born approximation, a linear differential Phase Contrast (DPC) model is built between 3D complex index of refraction and the intensity stacks. Therefore, 3D volume reconstruction can be achieved via a fast inversion method, without the intermediate 2D Phase retrieval step. Our system employs spatially partially coherent illumination, so the transverse resolution achieves twice the NA of coherent systems, while axial resolution is also improved 2× as compared to holographic imaging.
-
3d differential Phase Contrast Microscopy with computational illumination using an led array
Optics Letters, 2014Co-Authors: Lei Tian, Jingyan Wang, Laura WallerAbstract:We demonstrate 3D differential Phase-Contrast (DPC) Microscopy, based on computational illumination with a programmable LED array. By capturing intensity images with various illumination angles generated by sequentially patterning an LED array source, we digitally refocus images through various depths via light field processing. The intensity differences from images taken at complementary illumination angles are then used to generate DPC images, which are related to the gradient of Phase. The proposed method achieves 3D DPC with simple, inexpensive optics and no moving parts. We experimentally demonstrate our method by imaging a camel hair sample in 3D.
Paul Michael William French - One of the best experts on this subject based on the ideXlab platform.
-
single shot Phase Contrast Microscopy using polarisation resolved differential Phase Contrast
Journal of Biophotonics, 2021Co-Authors: R Kalita, W Flanagan, Jonathan Lightley, Sunil Kumar, Yuriy Alexandrov, Edwin Garcia, Mark Hintze, Michalis Barkoulas, Christopher Dunsby, Paul Michael William FrenchAbstract:We present a robust, low-cost single-shot implementation of differential Phase Microscopy utilising a polarisation-sensitive camera to simultaneously acquire 4 images from which Phase Contrast images can be calculated. This polarisation-resolved differential Phase Contrast (pDPC) Microscopy technique can be easily integrated with fluorescence Microscopy. This article is protected by copyright. All rights reserved.