AIMC Topic: Acrosome

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Deep learning classification method for boar sperm morphology analysis.

Andrology
BACKGROUND: Boar 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 subj...

Impact of transfer learning for human sperm segmentation using deep learning.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Infertility affects approximately one in ten couples, and almost half of the infertility cases are due to the malefactor. To diagnose infertility and determine future treatment, a semen analysis is performed. Evaluation of s...

A novel deep learning method for automatic assessment of human sperm images.

Computers in biology and medicine
Sperm morphology analysis (SMA) is a very important factor in the diagnosis process of male infertility. This research proposes a novel deep learning algorithm for malformation detection of sperm morphology using human sperm cell images. Our proposed...

Deep Learning Models for Multi-Part Morphological Segmentation and Evaluation of Live Unstained Human Sperm.

Sensors (Basel, Switzerland)
To perform accurate computer vision quality assessments of sperm used within reproductive medicine, a clear separation of each sperm component from the background is critical. This study systematically evaluates and compares the performance of Mask R...

Effect of Cholesterol-loaded Cyclodextrin on Membrane and Acrosome Status of Hariana Bull Sperm during Cryopreservation.

Cryo letters
BACKGROUND: The membrane and acrosomal integrity of sperm play a vital role in fertilization process; however they are compromised upon cryopreservation.