Sperm Morphology Analysis with CNN based algorithms

Savkay, O. Levent; Cesur, Evren; Yalçın, Müştak E.; Tavşanoglu, V.

doi:10.1109/cnna.2014.6888647

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Javadi and Mirroshandel [32] provide a deep learning approach, applying a convolutional deep neural network to the Sperm Morphology Analysis dataset (MHSMA). Savkay et al [33] present a convolutional neural network approach to CASA, presenting various morphological parameter predictions. An improved convolutional deep neural network approach is presented in [34].…”

Section: Introductionmentioning

confidence: 99%

Image Trinarization Using a Partial Differential Equations: A Novel Approach to Automatic Sperm Image Analysis

Jacobs¹

2022

Preprint

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Partial differential equations have recently garnered substantial attention as an image processing framework due to their extensibility, the ability to rigorously engineer and analyse the governing dynamics as well as the ease of implementation using numerical methods. This paper explores a novel approach to image trinarization with a concrete real-world application of classifying regions of sperm images used in the automatic analysis of sperm morphology. The proposed methodology engineers a diffusion equation with non-linear source term, exhibiting three steady-states. The model is implemented as an image processor using a standard finite difference method to illustrate the efficacy of the proposed approach. The performance of the proposed approach is benchmarked against standard image clustering/segmentation methods and shown to be highly effective.

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Section: Introductionmentioning

confidence: 99%

Image Trinarization Using a Partial Differential Equations: A Novel Approach to Automatic Sperm Image Analysis

Jacobs¹

2022

Preprint

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Image trinarization using a partial differential equation: A novel approach to automatic sperm image analysis

Jacobs

2024

Applied Mathematical Modelling

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Deep learning classification method for boar sperm morphology analysis

Keller,

Maus,

Keller

et al. 2024

Andrology

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BackgroundBoar semen quality emphasizes three major criteria: sperm concentration, motility, and morphology. Methods to analyze concentration and motility quickly and objectively readily exist, but few exist for analyzing morphology outside of subjective manual counting. Other vital factors for fertilization, like acrosome health, lack efficient detection methods due to limitations in detection by the human eye and costly biomarker analysis, which is rarely used in semen diagnostics.ObjectiveTo overcome these challenges, we propose a novel approach integrating deep‐learning technology with high‐throughput image‐based flow cytometry (IBFC) for objective and accurate analysis of both morphology and label‐free acrosome health of thousands of individual spermatozoa at once, as opposed to manually counting on a microscope slide.Materials and methodsImages of 10,000 spermatozoa were captured using an IBFC and manually annotated based on the primary morphological defect or acrosome health status for the training of the convolutional neural network (CNN). The CNN used these images to train and then applied that training to unannotated images to predict the model accuracy.ResultsUsing the CNNs, high F1 scores of 96.73%, 98.55%, and 99.31% for 20x, 40x, and 60x magnifications, respectively, for morphological classification were attained. Additionally, the model demonstrates an F1 score of 99.8% in detecting subtle acrosome health variations at the 60x magnification.Discussion and conclusionsWe have established an integrated approach to rapidly collect and classify morphological defects and acrosome health status, without the use of manual counting or biomarker labeling. Our study underscores the potential of artificial intelligence in semen diagnostics, reducing technician variability, streamlining assays, and facilitating the development of additional label‐free detection methods. This innovative approach addresses the barriers hindering biomarker adoption in semen analysis, offering a promising avenue for enhancing reproductive health assessments.

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Sperm Morphology Analysis with CNN based algorithms

Cited by 3 publications

References 8 publications

Image Trinarization Using a Partial Differential Equations: A Novel Approach to Automatic Sperm Image Analysis

Image Trinarization Using a Partial Differential Equations: A Novel Approach to Automatic Sperm Image Analysis

Image trinarization using a partial differential equation: A novel approach to automatic sperm image analysis

Deep learning classification method for boar sperm morphology analysis

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