2 research outputs found
An unsupervised long short-term memory neural network for event detection in cell videos
We propose an automatic unsupervised cell event detection and classification
method, which expands convolutional Long Short-Term Memory (LSTM) neural
networks, for cellular events in cell video sequences. Cells in images that are
captured from various biomedical applications usually have different shapes and
motility, which pose difficulties for the automated event detection in cell
videos. Current methods to detect cellular events are based on supervised
machine learning and rely on tedious manual annotation from investigators with
specific expertise. So that our LSTM network could be trained in an
unsupervised manner, we designed it with a branched structure where one branch
learns the frequent, regular appearance and movements of objects and the second
learns the stochastic events, which occur rarely and without warning in a cell
video sequence. We tested our network on a publicly available dataset of
densely packed stem cell phase-contrast microscopy images undergoing cell
division. This dataset is considered to be more challenging that a dataset with
sparse cells. We compared our method to several published supervised methods
evaluated on the same dataset and to a supervised LSTM method with a similar
design and configuration to our unsupervised method. We used an F1-score, which
is a balanced measure for both precision and recall. Our results show that our
unsupervised method has a higher or similar F1-score when compared to two fully
supervised methods that are based on Hidden Conditional Random Fields (HCRF),
and has comparable accuracy with the current best supervised HCRF-based method.
Our method was generalizable as after being trained on one video it could be
applied to videos where the cells were in different conditions. The accuracy of
our unsupervised method approached that of its supervised counterpart
Semi-supervised estimation of event temporal length for cell event detection
Cell event detection in cell videos is essential for monitoring of cellular
behavior over extended time periods. Deep learning methods have shown great
success in the detection of cell events for their ability to capture more
discriminative features of cellular processes compared to traditional methods.
In particular, convolutional long short-term memory (LSTM) models, which
exploits the changes in cell events observable in video sequences, is the
state-of-the-art for mitosis detection in cell videos. However, their
limitations are the determination of the input sequence length, which is often
performed empirically, and the need for a large annotated training dataset
which is expensive to prepare. We propose a novel semi-supervised method of
optimal length detection for mitosis detection with two key contributions: (i)
an unsupervised step for learning the spatial and temporal locations of cells
in their normal stage and approximating the distribution of temporal lengths of
cell events and, (ii) a step of inferring, from that distribution, an optimal
input sequence length and a minimal number of annotated frames for training a
LSTM model for each particular video. We evaluated our method in detecting
mitosis in densely packed stem cells in a phase-contrast microscopy videos. Our
experimental data prove that increasing the input sequence length of LSTM can
lead to a decrease in performance. Our results also show that by approximating
the optimal input sequence length of the tested video, a model trained with
only 18 annotated frames achieved F1-scores of 0.880-0.907, which are 10%
higher than those of other published methods with a full set of 110 training
annotated frames