Vessel enhancement with multiscale and curvilinear filter matching for placenta images

Park, Mary; Yampolsky, Michael; Shlakhter, Oleksandr; VanHorn, Samantha; Dygulska, Beata; Kiryankova, Nora; Inany, H.; Charlagorla, P.; Salafia, Carolyn M.

doi:10.1016/j.placenta.2013.06.041

Cited by 2 publications

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“…Before implementing of these modules of our Python package, the user needs to perform foreground-background separation. By our experience, the most efficient methods are simple thresholding and Frangi filter-based thresholding ( Frangi et al, 1998 ; Hemler et al, 2004 ; Shi and Yang, 2009 ; Park et al, 2013 ; Comin et al, 2014 ). Simple thresholding efficiently processes images with small numbers of fibers and low levels of background.…”

Section: Resultsmentioning

confidence: 99%

Integrated Cells and Collagen Fibers Spatial Image Analysis

et al. 2021

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Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and mass spectrometry imaging (MSI) provide large amounts of quantitative and spatial information about cells and tissue structures like vessels, bronchioles etc. Many published reports have demonstrated that the structural features of cells and extracellular matrix (ECM) and their interactions strongly predict disease development and progression. Computational image analysis methods in combination with spatial analysis and machine learning can reveal novel structural patterns in normal and diseased tissue. Here, we have developed a Python package designed for integrated analysis of cells and ECM in a spatially dependent manner. The package performs segmentation, labeling and feature analysis of ECM fibers, combines this information with pre-generated single-cell based datasets and realizes cell-cell and cell-fiber spatial analysis. To demonstrate performance and compatibility of our computational tool, we integrated it with a pipeline designed for cell segmentation, classification, and feature analysis in the KNIME analytical platform. For validation, we used a set of mouse mammary gland tumors and human lung adenocarcinoma tissue samples stained for multiple cellular markers and collagen as the main ECM protein. The developed package provides sufficient performance and precision to be used as a novel method to investigate cell-ECM relationships in the tissue, as well as detect structural patterns correlated with specific disease outcomes.

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Section: Resultsmentioning

confidence: 99%

Integrated Cells and Collagen Fibers Spatial Image Analysis

et al. 2021

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“…There are existing studies that describe methods for segmenting placental blood vessels. Almoussa et al [14], Park et al [15], and Chang et al [16] describe methods for segmenting blood vessels within images of ex vivo placentas. The segmentation of ex vivo placentas is useful for applications in pathology, such as the postpartum diagnosis of placental diseases by analyzing the structure of the placental vascular network.…”

Section: Introductionmentioning

confidence: 99%

Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery

et al. 2018

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Introduction Twin-to-twin transfusion syndrome (TTTS) is a potentially lethal condition that affects pregnancies in which twins share a single placenta. The definitive treatment for TTTS is fetoscopic laser photocoagulation, a procedure in which placental blood vessels are selectively cauterized. Challenges in this procedure include difficulty in quickly identifying placental blood vessels due to the many artifacts in the endoscopic video that the surgeon uses for navigation. We propose using deep-learned segmentations of blood vessels to create masks that can be recombined with the original fetoscopic video frame in such a way that the location of placental blood vessels is discernable at a glance. Methods In a process approved by an institutional review board, intraoperative videos were acquired from ten fetoscopic laser photocoagulation surgeries performed at Yale New Haven Hospital. A total of 345 video frames were selected from these videos at regularly spaced time intervals. The video frames were segmented once by an expert human rater (a clinician) and once by a novice, but trained human rater (an undergraduate student). The segmentations were used to train a fully convolutional neural network of 25 layers. Results The neural network was able to produce segmentations with a high similarity to ground truth segmentations produced by an expert human rater (sensitivity=92.15%±10.69%) and produced segmentations that were significantly more accurate than those produced by a novice human rater (sensitivity=56.87%±21.64%; p < 0.01). Conclusion A convolutional neural network can be trained to segment placental blood vessels with near-human accuracy and can exceed the accuracy of novice human raters. Recombining these segmentations with the original fetoscopic video frames can produced enhanced frames in which blood vessels are easily detectable. This has significant implications for aiding fetoscopic surgeons—especially trainees who are not yet at an expert level.

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Vessel enhancement with multiscale and curvilinear filter matching for placenta images

Cited by 2 publications

References 0 publications

Integrated Cells and Collagen Fibers Spatial Image Analysis

Integrated Cells and Collagen Fibers Spatial Image Analysis

Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery

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