TITLE: VISEM-Tracking, a human spermatozoa tracking dataset 

ABSTRACT: A manual assessment of sperm motility requires microscopy observation, which
is challenging due to the fast-moving spermatozoa in the field of view. To
obtain correct results, manual evaluation requires extensive training.
Therefore, computer-assisted sperm analysis (CASA) has become increasingly used
in clinics. Despite this, more data is needed to train supervised machine
learning approaches in order to improve accuracy and reliability in the
assessment of sperm motility and kinematics. In this regard, we provide a
dataset called VISEM-Tracking with 20 video recordings of 30 seconds
(comprising 29,196 frames) of wet sperm preparations with manually annotated
bounding-box coordinates and a set of sperm characteristics analyzed by experts
in the domain. In addition to the annotated data, we provide unlabeled video
clips for easy-to-use access and analysis of the data via methods such as self-
or unsupervised learning. As part of this paper, we present baseline sperm
detection performances using the YOLOv5 deep learning (DL) model trained on the
VISEM-Tracking dataset. As a result, we show that the dataset can be used to
train complex DL models to analyze spermatozoa.
A manual assessment of sperm motility requires microscopy observation, which
is challenging due to the fast-moving spermatozoa in the field of view. To
obtain correct results, manual evaluation requires extensive training.
Therefore, computer-assisted sperm analysis (CASA) has become increasingly used
in clinics. Despite this, more data is needed to train supervised machine
learning approaches in order to improve accuracy and reliability in the
assessment of sperm motility and kinematics. In this regard, we provide a
dataset called VISEM-Tracking with 20 video recordings of 30 seconds
(comprising 29,196 frames) of wet sperm preparations with manually annotated
bounding-box coordinates and a set of sperm characteristics analyzed by experts
in the domain. In addition to the annotated data, we provide unlabeled video
clips for easy-to-use access and analysis of the data via methods such as self-
or unsupervised learning. As part of this paper, we present baseline sperm
detection performances using the YOLOv5 deep learning (DL) model trained on the
VISEM-Tracking dataset. As a result, we show that the dataset can be used to
train complex DL models to analyze spermatozoa.
VISEM-Tracking, a human spermatozoa tracking dataset
http://arxiv.org/abs/2212.02842v5