Three-dimensional reconstruction and quantification of cervical carcinoma invasion fronts from histological serial sections

Braumann, U.-D.; Kuska, Jens-Peer; Einenkel, Jens; Horn, Lars‐Christian; Löffler, Markus; Höckel, Michael

doi:10.1109/tmi.2005.855437

Cited by 52 publications

(46 citation statements)

References 34 publications

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“…This measure also helps in computing the other features like elongation and eccentricity. Cytoplasmic and Nuclear Orientation specifies the angle between the X-axis and the major axis of the ellipse that fits best the biological object of interest.Cytoplasm/Nuclear Compactness is used for calibration of shape and this parameter is widely utilized to describe the morphometric changes in the biological entities [29,30] …”

Section: Features Selected For Calibration Of Cervical Cells and Theimentioning

confidence: 99%

Novel Benchmark Database of Digitized and Calibrated Cervical Cells for Artificial Intelligence Based Screening of Cervical Cancer

Sarwar¹,

Suri²,

Sharma³

et al. 2017

Gynecol Obstet

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Objective: The primary objective of this research work is to develop a novel benchmark database of digitized and calibrated, cervical cells obtained from slides of Papanicolaou smear test, which is done for screening of cervical cancer. This database can serve as a potential tool for designing, developing, training, testing and validating various Artificial intelligence based systems for prognosis of cervical cancer by characterization and classification of Papanicolaou smear images. The database can also be used by other researchers for comparative analysis of working efficiencies of various machine learning and image processing algorithms. The database can be obtained by sending a request to the corresponding author. Besides developing a rich machine learning database we have also presented a novel artificial intelligence based hybrid ensemble technique for efficient screening of cervical cancer by automated analysis of Papanicolaou smear images. Methodology:The correct and timely diagnosis of cervical cancer is one of the major problems in the medical world. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. Papanicolaou smear (also referred to as Pap smear) is a microscopic examination of samples of human cells scraped from the lower, narrow part of the uterus, called cervix. A sample of cells after being stained by using Papanicolaou method is analyzed under microscope for the presence of any unusual developments indicating any precancerous and potentially precancerous developments. Abnormal findings, if observed are subjected to further precise diagnostic subroutines. Examining the cell images for abnormalities in the cervix provides grounds for provision of prompt action and thus reducing incidence and deaths from cervical cancer. It is the most popular technique used for screening of cervical cancer. Pap smear test, if done with a regular screening programs and proper follow-up, can reduce cervical cancer mortality by up to 80%. The contribution of this paper is that we have created a rich machine learning database of quantitatively profiled and calibrated cervical cells obtained from Papsmear test slides. The database so created consists of data of about 200 clinical cases (8091 cervical cells), which have been obtained from multiple health care centers, so as to ensure diversity in data. The slides were processed using a multi-headed digital microscope and images of cervical cells were obtained, which were passed through various data preprocessing subroutines. After preprocessing the cells were morphologically profiled and scaled to obtain separate quantitative measurements of various features of cytoplasm and nucleus respectively. The cells in the database were carefully classified in different corresponding classes according to latest 2001-Bethesda system of classification, by technicians. In addition to this, we have also pioneered to apply a novel hybrid ensemble system to this database in order to evaluate the effect...

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Section: Features Selected For Calibration Of Cervical Cells and Theimentioning

confidence: 99%

Novel Benchmark Database of Digitized and Calibrated Cervical Cells for Artificial Intelligence Based Screening of Cervical Cancer

Sarwar¹,

Suri²,

Sharma³

et al. 2017

Gynecol Obstet

View full text Add to dashboard Cite

show abstract

“…Typically, one segments each image into various components and regions and then achieves alignment across the images through a process of registration [3], [9]. Work presented in [8] follows this mantra successfully to achieve tangible reconstructions.…”

Section: Related Workmentioning

confidence: 99%

Reconstruction of Cellular Biological Structures from Optical Microscopy Data

Mosaliganti

Cooper

Sharp

et al. 2008

IEEE Trans. Visual. Comput. Graphics

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Abstract-Developments in optical microscopy imaging have generated large high-resolution datasets that have spurred medical researchers to conduct investigations into mechanisms of disease, including cancer, at cellular and sub-cellular levels. The work reported here demonstrates that a suitable methodology can be conceived which isolates modality-dependent effects from the larger segmentation task and that 3D reconstructions can be cognizant of shapes as evident in the available 2D planar images. In the current realization, a method based on active geodesic contours is first deployed to counter the ambiguity that exists in separating overlapping cells on the image plane. Later, another segmentation effort based on a variant of Voronoi tessellations improves the delineation of the cell boundaries using a Bayesian formulation. In the next stage, the cells are interpolated across the third dimension thereby mitigating the poor structural correlation that exists in that dimension. We deploy our methods on three separate datasets obtained from light, confocal and phase-contrast bright field microscopy and validate the results appropriately.

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“…Large scale image registration has many applications in both biomedical research [10,22,32] and geophysics [33]. However, there are currently few works addressing image registration algorithms intended to run efficiently on high performance computing environments.…”

Section: Related Workmentioning

confidence: 99%

Non-rigid Registration for Large Sets of Microscopic Images on Graphics Processors

Ruiz

Ujaldón

Cooper

et al. 2008

J Sign Process Syst Sign Image Video Technol

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Microscopic imaging is an important tool for characterizing tissue morphology and pathology. 3D reconstruction and visualization of large sample tissue structure requires registration of large sets of high-resolution images. However, the scale of this problem presents a challenge for automatic registration methods. In this paper we present a novel method for efficient automatic registration using graphics processing units (GPUs) and parallel programming. Comparing a C++ CPU implementation with Compute Unified Device Architecture (CUDA) libraries and pthreads running on GPU we achieve a speed-up factor of up to 4.11× with a single GPU and 6.68× with a GPU pair. We present execution times for a benchmark composed of two sets of large-scale images: mouse placenta (16K × 16K pixels) and breast cancer tumors (23K × 62K pixels). It takes more than 12 hours for the genetic case in C++ to register a typical sample composed of 500 consecutive slides, which was reduced to less than 2 hours using two GPUs, in addition to a very promising scalability for extending those gains easily on a large number of GPUs in a distributed system.

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Three-dimensional reconstruction and quantification of cervical carcinoma invasion fronts from histological serial sections

Cited by 52 publications

References 34 publications

Novel Benchmark Database of Digitized and Calibrated Cervical Cells for Artificial Intelligence Based Screening of Cervical Cancer

Novel Benchmark Database of Digitized and Calibrated Cervical Cells for Artificial Intelligence Based Screening of Cervical Cancer

Reconstruction of Cellular Biological Structures from Optical Microscopy Data

Non-rigid Registration for Large Sets of Microscopic Images on Graphics Processors

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