Image Segmentation of Zona-Ablated Human Blastocysts

• URL: http://arxiv.org/abs/2008.08673v1
• Date: Wed, 19 Aug 2020 21:20:02 GMT
• Title: Image Segmentation of Zona-Ablated Human Blastocysts
• Authors: Md Yousuf Harun, M Arifur Rahman, Joshua Mellinger, Willy Chang, Thomas Huang, Brienne Walker, Kristen Hori, and Aaron T. Ohta
• Abstract summary: The goal of this work is to facilitate the challenging task of segmenting irregularly shaped blastocysts. The experimental results on the test set demonstrate segmentation greatly improves the accuracy of expansion measurements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automating human preimplantation embryo grading offers the potential for higher success rates with in vitro fertilization (IVF) by providing new quantitative and objective measures of embryo quality. Current IVF procedures typically use only qualitative manual grading, which is limited in the identification of genetically abnormal embryos. The automatic quantitative assessment of blastocyst expansion can potentially improve sustained pregnancy rates and reduce health risks from abnormal pregnancies through a more accurate identification of genetic abnormality. The expansion rate of a blastocyst is an important morphological feature to determine the quality of a developing embryo. In this work, a deep learning based human blastocyst image segmentation method is presented, with the goal of facilitating the challenging task of segmenting irregularly shaped blastocysts. The type of blastocysts evaluated here has undergone laser ablation of the zona pellucida, which is required prior to trophectoderm biopsy. This complicates the manual measurements of the expanded blastocyst's size, which shows a correlation with genetic abnormalities. The experimental results on the test set demonstrate segmentation greatly improves the accuracy of expansion measurements, resulting in up to 99.4% accuracy, 98.1% precision, 98.8% recall, a 98.4% Dice Coefficient, and a 96.9% Jaccard Index.

Related papers

• Karyotype AI for Precision Oncology [24.283441582734255]
  Efforts to automate karyotype analysis to date fell short in aberration detection. TopViT outperformed CNN (Inception) models with >99.3% accuracy for chromosome identification.
  arXiv  Detail & Related papers  (2022-11-20T04:59:23Z)
• Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps [48.498376125522114]
  This paper proposes a UNet-based segmentation approach that requires minimal post-processing. It achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy.
  arXiv  Detail & Related papers  (2022-10-13T15:34:20Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences [0.0]
  We investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model.
  arXiv  Detail & Related papers  (2021-03-12T13:36:30Z)
• Cancer Gene Profiling through Unsupervised Discovery [49.28556294619424]
  We introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. Our signature reports promising results on distinguishing immune inflammatory and immune desert tumors.
  arXiv  Detail & Related papers  (2021-02-11T09:04:45Z)
• Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation [44.02622933605018]
  Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM) We aimed to compare nine machine learning classifiers to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients. xGB obtained maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,
  arXiv  Detail & Related papers  (2021-02-10T15:10:37Z)
• Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients [1.739079346425631]
  Sickle Cell Disease (SCD) is one of the most common genetic diseases in the world. Current workflow to measure spleen size includes palpation, possibly followed by manual length measurement in 2D ultrasound imaging. We investigate the use of deep learning to perform automatic estimation of spleen length from ultrasound images.
  arXiv  Detail & Related papers  (2020-09-06T10:47:49Z)
• Human Blastocyst Classification after In Vitro Fertilization Using Deep Learning [0.0]
  This study includes a total of 1084 images from 1226 embryos. The images were labelled based on Veeck criteria that differentiate embryos to grade 1 to 5 based on the size of the blastomere and the grade of fragmentation. Our best model from fine-tuning a pre-trained ResNet50 on the dataset results in 91.79% accuracy.
  arXiv  Detail & Related papers  (2020-08-28T04:40:55Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)
• Deep learning mediated single time-point image-based prediction of embryo developmental outcome at the cleavage stage [1.6753684438635652]
  Cleavage stage transfers are beneficial for patients with poor prognosis and at fertility centers in resource-limited settings. Time-lapse imaging systems have been proposed as possible solutions, but they are cost-prohibitive and require bulky and expensive hardware. Here, we report an automated system for classification and selection of human embryos at the cleavage stage using a trained CNN combined with a genetic algorithm.
  arXiv  Detail & Related papers  (2020-05-21T21:21:15Z)
• Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes [52.53375964591765]
  We propose the first fully-automated solution to segment the whole fetal head in US volumes. The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features.
  arXiv  Detail & Related papers  (2020-04-28T14:43:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.