Modified histogram-based segmentation and adaptive distance tracking of sperm cells image sequences

Kheirkhah, Fateme Mostajer; Mohammadi, Hamid Reza; Shahverdi, Abdolhossein

doi:10.1016/j.cmpb.2017.11.005

Cited by 10 publications

(10 citation statements)

References 6 publications

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“…There are several reports on tracking motile sperm using deep learning, 20 , 21 , 22 , 23 of which the latest and highest performing ones were reported by Somasundaram et al 12 The sperm with the fastest movement speed was detected in 1.12 se, and the error rate was 2.31 with high accuracy. Those authors dealt with vertical defocus by using a cover glass to drop unstained sperm.…”

Section: Discussionmentioning

confidence: 99%

A new deep‐learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure

Sato

Kishi

Murakata

et al. 2022

Reprod Medicine & Biology

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Purpose To create and evaluate a machine‐learning model for YOLOv3 that can simultaneously perform morphological evaluation and tracking in a short time, which can be adapted to video data under an inverted microscope. Methods Japanese patients who underwent intracytoplasmic sperm injection at the Jikei University School of Medicine and Keiai Reproductive and Endosurgical Clinic from January 2019 to March 2020 were included. An AI model that simultaneously performs morphological assessment and tracking was created and its performance was evaluated. Results For morphological assessment, the sensitivity and positive predictive value (PPV) of this model for abnormal sperm were 0.881 and 0.853, respectively. The sensitivity and PPV for normal sperm were 0.794 and 0.689, respectively. For tracking performance, among the 51 objects, 40 (78.4%) were mostly tracked, 11 (21.6%) were partially tracked, and 0 (0%) were mostly lost. Conclusions This study showed that evaluating sperm morphology while tracking in a single model is possible by training YOLO v3. This model could acquire time‐series data of one sperm, which will assist in acquiring and annotating sperm image data.

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Section: Discussionmentioning

confidence: 99%

A new deep‐learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure

Sato

Kishi

Murakata

et al. 2022

Reprod Medicine & Biology

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“…Later, the remaining steps of the AWAS algorithm are applied to extract the sperm cells trajectories [22]. Finally, the transition matrix,

TR

, is obtained which each rows of that represents the path (indices) of one sperm cell along the frames of the video stream.…”

Section: Methodsmentioning

confidence: 99%

“…where A is a constant value, H(x) and H Q (x) are the amplitudes of the histogram and quasi-histogram diagrams of the image, respectively. In this study, the value of 1/ A is set equal to 150 [22].…”

Section: Sperm Cells Detectionmentioning

confidence: 99%

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Efficient and robust segmentation and tracking of sperm cells in microscopic image sequences

Kheirkhah

Mohammadi

Shahverdi³

2019

IET Computer Vision

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Sperm motility analysis is an important factor in male fertility diagnosis. This article presents a hybrid segmentation method to detect sperm cells, which is robust to density variation of the cells in the image sequences. In addition, a preprocessing scheme is employed to remove fixed sperm cells and debris, which facilitate and speed up the cells' tracking stage. The article also proposes an automated sperm-tracking algorithm in semen samples image sequences. It is a multi-step tracking scheme, which is an enhanced version of adaptive window average speed (AWAS) tracking algorithm. It retrieves lost sperm cells during the tracking stage in adjacent frames and alleviates the cells collide problem. The proposed tracking algorithm provides both superior accuracy and higher speed compared to those of the other competitive algorithms for image sequences regardless of their particle densities.

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“…Kheirkhah et al [ 26 ] suggested a method for sperm detection and tracking that is based on Adaptive Distance Tracking (ADT). In this method, the sperm is located by detecting its centroid, and then, its coordinates are determined in each frame.…”

Section: Research Backgroundmentioning

confidence: 99%

Multistage Optimization Using a Modified Gaussian Mixture Model in Sperm Motility Tracking

Alameri

Hasikin‬

Kadri

et al. 2021

Computational and Mathematical Methods in Medicine

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Infertility is a condition whereby pregnancy does not occur despite having unprotected sexual intercourse for at least one year. The main reason could originate from either the male or the female, and sometimes, both contribute to the fertility disorder. For the male, sperm disorder was found to be the most common reason for infertility. In this paper, we proposed male infertility analysis based on automated sperm motility tracking. The proposed method worked in multistages, where the first stage focused on the sperm detection process using an improved Gaussian Mixture Model. A new optimization protocol was proposed to accurately detect the motile sperms prior to the sperm tracking process. Since the optimization protocol was imposed in the proposed system, the sperm tracking and velocity estimation processes are improved. The proposed method attained the highest average accuracy, sensitivity, and specificity of 92.3%, 96.3%, and 72.4%, respectively, when tested on 10 different samples. Our proposed method depicted better sperm detection quality when qualitatively observed as compared to other state-of-the-art techniques.

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Modified histogram-based segmentation and adaptive distance tracking of sperm cells image sequences

Cited by 10 publications

References 6 publications

A new deep‐learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure

A new deep‐learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure

Efficient and robust segmentation and tracking of sperm cells in microscopic image sequences

Multistage Optimization Using a Modified Gaussian Mixture Model in Sperm Motility Tracking

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