3 research outputs found
Convolutional neural networks for segmentation and object detection of human semen
We compare a set of convolutional neural network (CNN) architectures for the
task of segmenting and detecting human sperm cells in an image taken from a
semen sample. In contrast to previous work, samples are not stained or washed
to allow for full sperm quality analysis, making analysis harder due to
clutter. Our results indicate that training on full images is superior to
training on patches when class-skew is properly handled. Full image training
including up-sampling during training proves to be beneficial in deep CNNs for
pixel wise accuracy and detection performance. Predicted sperm cells are found
by using connected components on the CNN predictions. We investigate
optimization of a threshold parameter on the size of detected components. Our
best network achieves 93.87% precision and 91.89% recall on our test dataset
after thresholding outperforming a classical mage analysis approach.Comment: Submitted for Scandinavian Conference on Image Analysis 201
A review of different deep learning techniques for sperm fertility prediction
Sperm morphology analysis (SMA) is a significant factor in diagnosing male infertility. Therefore, healthy sperm detection is of great significance in this process. However, the traditional manual microscopic sperm detection methods have the disadvantages of a long detection cycle, low detection accuracy in large orders, and very complex fertility prediction. Therefore, it is meaningful to apply computer image analysis technology to the field of fertility prediction. Computer image analysis can give high precision and high efficiency in detecting sperm cells. In this article, first, we analyze the existing sperm detection techniques in chronological order, from traditional image processing and machine learning to deep learning methods in segmentation and classification. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. Finally, the future development direction and challenges of sperm cell detection are discussed. We have summarized 44 related technical papers from 2012 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of fertility prediction and provide a reference for researchers in other fields