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New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images. Sci Rep 2015; 5:10690. [PMID: 26022540 PMCID: PMC4448264 DOI: 10.1038/srep10690] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2014] [Accepted: 04/22/2015] [Indexed: 12/30/2022] Open
Abstract
Computer-aided image analysis (CAI) can help objectively quantify morphologic features of hematoxylin-eosin (HE) histopathology images and provide potentially useful prognostic information on breast cancer. We performed a CAI workflow on 1,150 HE images from 230 patients with invasive ductal carcinoma (IDC) of the breast. We used a pixel-wise support vector machine classifier for tumor nests (TNs)-stroma segmentation, and a marker-controlled watershed algorithm for nuclei segmentation. 730 morphologic parameters were extracted after segmentation, and 12 parameters identified by Kaplan-Meier analysis were significantly associated with 8-year disease free survival (P < 0.05 for all). Moreover, four image features including TNs feature (HR 1.327, 95%CI [1.001 - 1.759], P = 0.049), TNs cell nuclei feature (HR 0.729, 95%CI [0.537 - 0.989], P = 0.042), TNs cell density (HR 1.625, 95%CI [1.177 - 2.244], P = 0.003), and stromal cell structure feature (HR 1.596, 95%CI [1.142 - 2.229], P = 0.006) were identified by multivariate Cox proportional hazards model to be new independent prognostic factors. The results indicated that CAI can assist the pathologist in extracting prognostic information from HE histopathology images for IDC. The TNs feature, TNs cell nuclei feature, TNs cell density, and stromal cell structure feature could be new prognostic factors.
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An ensemble-averaged, cell density-based digital model of zebrafish embryo development derived from light-sheet microscopy data with single-cell resolution. Sci Rep 2015; 5:8601. [PMID: 25712513 PMCID: PMC5390106 DOI: 10.1038/srep08601] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Accepted: 01/13/2015] [Indexed: 11/13/2022] Open
Abstract
A new era in developmental biology has been ushered in by recent advances in the quantitative imaging of all-cell morphogenesis in living organisms. Here we have developed a light-sheet fluorescence microscopy-based framework with single-cell resolution for identification and characterization of subtle phenotypical changes of millimeter-sized organisms. Such a comparative study requires analyses of entire ensembles to be able to distinguish sample-to-sample variations from definitive phenotypical changes. We present a kinetic digital model of zebrafish embryos up to 16 h of development. The model is based on the precise overlay and averaging of data taken on multiple individuals and describes the cell density and its migration direction at every point in time. Quantitative metrics for multi-sample comparative studies have been introduced to analyze developmental variations within the ensemble. The digital model may serve as a canvas on which the behavior of cellular subpopulations can be studied. As an example, we have investigated cellular rearrangements during germ layer formation at the onset of gastrulation. A comparison of the one-eyed pinhead (oep) mutant with the digital model of the wild-type embryo reveals its abnormal development at the onset of gastrulation, many hours before changes are obvious to the eye.
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Widmer C, Heinrich S, Drewe P, Lou X, Umrania S, Rätsch G. Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images. SIGNAL, IMAGE AND VIDEO PROCESSING 2014; 8:41-48. [PMID: 25866587 PMCID: PMC4389647 DOI: 10.1007/s11760-014-0694-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Analysis of microscopy images can provide insight into many biological processes. One particularly challenging problem is cellular nuclear segmentation in highly anisotropic and noisy 3D image data. Manually localizing and segmenting each and every cellular nucleus is very time-consuming, which remains a bottleneck in large-scale biological experiments. In this work, we present a tool for automated segmentation of cellular nuclei from 3D fluorescent microscopic data. Our tool is based on state-of-the-art image processing and machine learning techniques and provides a user-friendly graphical user interface. We show that our tool is as accurate as manual annotation and greatly reduces the time for the registration.
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Affiliation(s)
| | - Stephanie Heinrich
- Institute for Biochemistry, ETH Zurich, Otto-Stern-Weg 3, Zurich, Switzerland
| | - Philipp Drewe
- Sloan Kettering Institute, 1275 York avenue, New York, NY, USA
| | - Xinghua Lou
- Sloan Kettering Institute, 1275 York avenue, New York, NY, USA
| | - Shefali Umrania
- Sloan Kettering Institute, 1275 York avenue, New York, NY, USA
| | - Gunnar Rätsch
- Sloan Kettering Institute, 1275 York avenue, New York, NY, USA
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He Y, Meng Y, Gong H, Chen S, Zhang B, Ding W, Luo Q, Li A. An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm. PLoS One 2014; 9:e104437. [PMID: 25111442 PMCID: PMC4128780 DOI: 10.1371/journal.pone.0104437] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 07/14/2014] [Indexed: 11/25/2022] Open
Abstract
Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1) concave points clustering to determine the seed points of touching cells; and 2) random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.
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Affiliation(s)
- Yong He
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yunlong Meng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Bin Zhang
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenxiang Ding
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail:
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