Ensembled Deep Learning for the Classification of Human Sperm Head Morphology

Lindsay, Spencer; Fernando, Jared E. M.; Akbaridoust, Farzan; Ackermann, Klaus; Nosrati, Reza

doi:10.1002/aisy.202200111

Cited by 11 publications

(7 citation statements)

References 51 publications

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“…Eventually, integrating our model with an automated selection system for sperm cells based on their motility, morphology, or DNA fragmentation [12][13][14][15][16][17]41,42] is another important step in the path for an accurate, efficient, fully automatic sperm selection framework for ICSI, which eventually will lead to higher success rate.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…[8][9][10][11] Computerized motility feature extraction [12] facilitates fast detection and quality analysis of sperm cells. Researchers have also explored the use of various microscopic imaging techniques, including quantitative phase imaging and deep learning algorithms, to predict the quality of sperm cells based on their morphology and estimate the extent of their DNA fragmentation, [13][14][15][16] in addition to their motility. [17] Yet, sperm cell tracking faces many challenges such as overlaying of cells in the semen sample and sperm cells that get out of focus during motion.…”

Section: Introductionmentioning

confidence: 99%

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Location Prediction of Sperm Cells Using Long Short‐Term Memory Networks

Noy

Barnea

Dudaie

et al. 2023

Advanced Intelligent Systems

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Intracytoplasmic sperm injection (ICSI) requires precise selection of a single sperm cell in a dish to be injected into an oocyte. This task is challenging due to high sperm velocity, collision with other sperm cells, and changes in the imaging focus. Herein, a new model is proposed, which is based on a multilayer long short‐term memory (LSTM) network combined with linear extrapolation calculation, for predicting the future location of individual sperm cells based on their previous paths. The model is trained with a unique loss function, constructed from the predicted location and trajectory, and results in low mean location error predictions. The results are based on comparing different input sequences length, number of time frames ahead, and motility parameters of sperm cells. This model can provide faster and more accurate sperm motility predictions, better tracking, and aid automatic sperm capturing technologies.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Location Prediction of Sperm Cells Using Long Short‐Term Memory Networks

Noy

Barnea

Dudaie

et al. 2023

Advanced Intelligent Systems

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“…Taking infertility as an example, which affects one in six couples worldwide [3,4], numerous deep learning models have been developed with the aim of improving clinical outcomes and optimizing the operational efficiency in in vitro fertilization (IVF) clinics [5][6][7][8]. Most of these models take images as input, for instance, to evaluate sperm motility, concentration, and morphology for selecting high-quality sperm for fertilization [9][10][11] or for diagnosing male infertility [12][13][14], to help identify and distinguish sperm and debris in testicular sperm samples [15,16], or to examine the quality of oocytes [17]. Models have also been developed to use embryo images or time-lapse videos to grade embryos [18,19] and to predict treatment outcomes such as implantation [20], pregnancy [21], and live birth [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Existing studies [12-15, 35-39, 43-45], were retrospective studies where a retrospectively collected dataset was split into training, validation, and testing sub-datasets. Although such datasets may include data from multiple clinics [10,11], model validation and testing were still performed under the same data collection conditions as the training dataset. The lack of prospective model validation and testing with new data beyond the retrospectively collected dataset challenges the reproducibility of the developed model under different clinical setups.…”

Section: Introductionmentioning

confidence: 99%

Testing the reproducibility and effectiveness of deep learning models among clinics: sperm detection as a pilot study

Wang,

Jin,

Jiang

et al. 2024

Preprint

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Background: Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the reproducibility of object detection models, using sperm analysis as a pilot example. Methods: Ablation studies were performed using state-of-the-art models for detecting human sperm to quantitatively assess how model precision (false-positive detection) and recall (missed detection) were affected by imaging magnification, imaging mode, and sample preprocessing protocols. The results led to the hypothesis that the richness of image acquisition conditions in a training dataset deterministically affects model reproducibility. The hypothesis was tested by first enriching the training dataset with a wide range of imaging conditions, then validated through internal blind tests on new samples and external multi-center clinical validations. Results: Ablation experiments revealed that removing subsets of data from the training dataset significantly reduced model precision. Removing raw sample images from the training dataset caused the largest drop in model precision, whereas removing 20x images caused the largest drop in model recall. by incorporating different imaging and sample preprocessing conditions into a rich training dataset, the model achieved an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94-0.99) for precision, and an ICC of 0.97 (95% CI: 0.93-0.99) for recall. Multi-center clinical validation showed no significant differences in model precision or recall across different clinics and applications. Conclusions: The results validated the hypothesis that the richness of data in the training dataset is a key factor impacting model reproducibility. These findings highlight the importance of diversity in a training dataset for model evaluation and suggest that future deep learning models in andrology and reproductive medicine should incorporate comprehensive feature sets for enhanced reproducibility across clinics.

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“…Color, size and texture information were not effective in sperm detection and are basically ineffective in sperm detection based on color, size and texture information. On the other hand, sperm cells have highly similar morphology, which greatly reduces the feasibility of detection algorithms to extract information from the morphology [ 10 , 11 , 12 ]. At the same time, semen samples are doped with a lot of epithelial cells or other impurities, which put forward higher requirements for accurate detection of sperm in video frames.…”

Section: Introductionmentioning

confidence: 99%

YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection

et al. 2023

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Sperm detection performance is particularly critical for sperm motility tracking. However, there are a large number of non-sperm objects, sperm occlusion and poorly detailed texture features in semen images, which directly affect the accuracy of sperm detection. To solve the problem of false detection and missed detection in sperm detection, a multi-sperm target detection model, Yolov5s-SA, with an SA attention mechanism is proposed based on the YOLOv5s algorithm. Firstly, a depthwise, separable convolution structure is used to replace the partial convolution of the backbone network, which can ensure stable precision and reduce the number of model parameters. Secondly, a new multi-scale feature fusion module is designed to enhance the perception of feature information to supplement the positional information and high-resolution of the deep feature map. Finally, the SA attention mechanism is integrated into the neck network before the output of the feature map to enhance the correlation between the feature map channels and improve the fine-grained feature fusion ability of YOLOv5s. Experimental results show that compared with various YOLO algorithms, the proposed algorithm improves the detection accuracy and speed to a certain extent. Compared with the YOLOv3, YOLOv3-spp, YOLOv5s and YOLOv5m models, the average accuracy increases by 18.1%, 15.2%, 6.9% and 1.9%, respectively. It can effectively reduce the missed detection of occluded sperm and achieve lightweight and efficient multi-sperm target detection.

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Ensembled Deep Learning for the Classification of Human Sperm Head Morphology

Cited by 11 publications

References 51 publications

Location Prediction of Sperm Cells Using Long Short‐Term Memory Networks

Location Prediction of Sperm Cells Using Long Short‐Term Memory Networks

Testing the reproducibility and effectiveness of deep learning models among clinics: sperm detection as a pilot study

YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection

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