3 research outputs found
Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches
Due to cellular heterogeneity, cell nuclei classification, segmentation, and
detection from pathological images are challenging tasks. In the last few
years, Deep Convolutional Neural Networks (DCNN) approaches have been shown
state-of-the-art (SOTA) performance on histopathological imaging in different
studies. In this work, we have proposed different advanced DCNN models and
evaluated for nuclei classification, segmentation, and detection. First, the
Densely Connected Recurrent Convolutional Network (DCRN) model is used for
nuclei classification. Second, Recurrent Residual U-Net (R2U-Net) is applied
for nuclei segmentation. Third, the R2U-Net regression model which is named
UD-Net is used for nuclei detection from pathological images. The experiments
are conducted with different datasets including Routine Colon Cancer(RCC)
classification and detection dataset, and Nuclei Segmentation Challenge 2018
dataset. The experimental results show that the proposed DCNN models provide
superior performance compared to the existing approaches for nuclei
classification, segmentation, and detection tasks. The results are evaluated
with different performance metrics including precision, recall, Dice
Coefficient (DC), Means Squared Errors (MSE), F1-score, and overall accuracy.
We have achieved around 3.4% and 4.5% better F-1 score for nuclei
classification and detection tasks compared to recently published DCNN based
method. In addition, R2U-Net shows around 92.15% testing accuracy in term of
DC. These improved methods will help for pathological practices for better
quantitative analysis of nuclei in Whole Slide Images(WSI) which ultimately
will help for better understanding of different types of cancer in clinical
workflow.Comment: 18 pages, 16 figures, 3 Table
DeepDistance: A Multi-task Deep Regression Model for Cell Detection in Inverted Microscopy Images
This paper presents a new deep regression model, which we call DeepDistance,
for cell detection in images acquired with inverted microscopy. This model
considers cell detection as a task of finding most probable locations that
suggest cell centers in an image. It represents this main task with a
regression task of learning an inner distance metric. However, different than
the previously reported regression based methods, the DeepDistance model
proposes to approach its learning as a multi-task regression problem where
multiple tasks are learned by using shared feature representations. To this
end, it defines a secondary metric, normalized outer distance, to represent a
different aspect of the problem and proposes to define its learning as
complementary to the main cell detection task. In order to learn these two
complementary tasks more effectively, the DeepDistance model designs a fully
convolutional network (FCN) with a shared encoder path and end-to-end trains
this FCN to concurrently learn the tasks in parallel. DeepDistance uses the
inner distances estimated by this FCN in a detection algorithm to locate
individual cells in a given image. For further performance improvement on the
main task, this paper also presents an extended version of the DeepDistance
model. This extended model includes an auxiliary classification task and learns
it in parallel to the two regression tasks by sharing feature representations
with them. Our experiments on three different human cell lines reveal that the
proposed multi-task learning models, the DeepDistance model and its extended
version, successfully identify cell locations, even for the cell line that was
not used in training, and improve the results of the previous deep learning
methods.Comment: Preprint submitted to Elsevie
Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks
We propose a 3D convolutional neural network to simultaneously segment and
detect cell nuclei in confocal microscopy images. Mirroring the co-dependency
of these tasks, our proposed model consists of two serial components: the first
part computes a segmentation of cell bodies, while the second module identifies
the centers of these cells. Our model is trained end-to-end from scratch on a
mouse parotid salivary gland stem cell nuclei dataset comprising 107 image
stacks from three independent cell preparations, each containing several
hundred individual cell nuclei in 3D. In our experiments, we conduct a thorough
evaluation of both detection accuracy and segmentation quality, on two
different datasets. The results show that the proposed method provides
significantly improved detection and segmentation accuracy compared to
state-of-the-art and benchmark algorithms. Finally, we use a previously
described test-time drop-out strategy to obtain uncertainty estimates on our
predictions and validate these estimates by demonstrating that they are
strongly correlated with accuracy.Comment: We were not able to reproduce the result