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

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

doi:10.1049/iet-cvi.2018.5662

Cited by 13 publications

(7 citation statements)

References 23 publications

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“…The suggested algorithm was compared with six other algorithms ENN‐JPDAF, JPDAF, PDAF, GNN, NN, and AWAS [17]. As shown in the results in Tables 6 and 7, the suggested algorithm outperforms the other algorithms.…”

Section: Resultsmentioning

confidence: 98%

“…The algorithms are the Exact Nearest Neighbour extension to the JPDAF (ENN-JPDAF), the joint probabilistic data association filter (JPDAF), the probabilistic data association filter (PDAF), global nearest neighbour (GNN) and NN nearest neighbour algorithms. The sixth algorithm is the adaptive window average speed algorithm (AWAS) [17]. The results for the algorithm are in Table 4.…”

Section: Performance Metricsmentioning

confidence: 99%

“…Wang et al compared four algorithms Nearest Neighbour (NN), probabilistic data association (PDA), joint probabilistic data association (JPDA) and joint probability data association filter with track-coalescence-avoiding (JPDA*) [16], and they said that (JPDA*) has the best performance, but it has high Computational Complexity. In 2019, Mostajer et al presented a new sperm cells tracking method [17]. It starts with detecting sperm cells by a hybrid segmentation method that depends on Kittler and modified-Kittler thresholding methods.…”

Section: Introductionmentioning

confidence: 99%

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A robust sperm cell tracking algorithm using uneven lighting image fixing and improved branch and bound algorithm

Alabdulla

Haşıloğlu

Aksu

2021

IET Image Processing

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An accurate and robust sperm cells tracking algorithm that is able to detect and track sperm cells in videos with high accuracy and efficiency is presented. It is fast enough to process approximately 30 frames per second. It can find the correct path and measure motility parameters for each sperm. It can also adapt with different types of images coming from different cameras and bad recording conditions. Specifically, a new way is offered to optimize uneven lighting images to improve sperm cells detection which gives us the ability to get more accurate tracking results. The shape of each detected object is used to specify collided sperms and utilized dynamic gates which become bigger and smaller according to the sperm cell's speed. For assigning tracks to the detected sperm cells positions an improved version of branch and bound algorithm which is faster than the normal one is offered. This sperm cells tracking algorithm outperforms many of the previous algorithms as it has lower error rate in both sperm detection and tracking. It is compared with six other algorithms, and it gives lower tracking error rates. This method will allow doctors and researchers to obtain sperm motility data instantly and accurately. How to cite this article: Alabdulla, AA, Haşıloglu, A, Aksu, EH: A robust sperm cell tracking algorithm using uneven lighting image fixing and improved branch and bound algorithm.

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Section: Resultsmentioning

confidence: 98%

Section: Performance Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A robust sperm cell tracking algorithm using uneven lighting image fixing and improved branch and bound algorithm

Alabdulla

Haşıloğlu

Aksu

2021

IET Image Processing

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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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“…Modern CASA systems "have been designed to objectively and quantitatively measure several aspects of sperm structure and function, aiming to provide high levels of intra-and inter-laboratory consistency" [1,2]. To achieve this aim, methods of noise filtering, image segmentation, localization, multi-object tracking, and machine learning were employed [3][4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Researchmentioning

confidence: 99%

An Assessment Tool for Computer-Assisted Semen Analysis (CASA) Algorithms

Choi

Alkhoury

Urbano

et al. 2022

Preprint

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Background: Computer-Assisted Semen Analysis (CASA) enables reliable analysis of semen images, and is designed to process large number of images with high consistency, accuracy, and repeatability. Design and testing of CASA algorithms can be accelerated greatly if reliable simulations of semen images under a variety of conditions and sample quality modes are available. Using lifelike simulation of semen images can quantify the performance of existing and proposed CASA algorithms, since the parameters of the simulated image are known and controllable. Methods: We present simulation models for sperm cell image and swimming modes observed in real 2D (top-down) images of sperm cells in laboratory specimen. The models simulate human sperm using four (4) types of swimming, namely linear mean, circular, hyperactive, and immotile (or dead). Results: The simulation models are used in studying algorithms for segmentation, localization, and tracking of sperm cells. Several segmentation and localization algorithms were tested under varying levels of noise, and then compared using precision, recall, and the optimal subpattern assignment (OSPA) metric. Images of real human semen sample were used to validate the segmentation and localization observations obtained from simulations. An example is given of sperm cell tracking on simulated semen images of cells using the different tracking algorithms (nearest neighbor (NN), global nearest neighbor (GNN), probabilistic data association filter (PDAF), and joint probabilistic data association filter (JPDAF)). Tracking performance was evaluated through multi-object tracking precision (MOTP) and multi-object tracking accuracy (MOTA). Conclusion: Simulation models enable objective assessments of semen image processing algorithms. We demonstrate the use of a new simulation tool to assess and compare segmentation, localization, and tracking methods. The simulation software allows testing along a large spectrum of parameter values that control the appearance and behavior of simulated semen images. Users can generate scenarios of different characteristics and assess the effectiveness of different CASA algorithms in these environments. The simulation was used to assess and compare algorithms for segmentation and tracking of sperm cells in semen images.

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

Cited by 13 publications

References 23 publications

A robust sperm cell tracking algorithm using uneven lighting image fixing and improved branch and bound algorithm

A robust sperm cell tracking algorithm using uneven lighting image fixing and improved branch and bound algorithm

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

An Assessment Tool for Computer-Assisted Semen Analysis (CASA) Algorithms

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