2,372 research outputs found
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images
The analysis of glandular morphology within colon histopathology images is an
important step in determining the grade of colon cancer. Despite the importance
of this task, manual segmentation is laborious, time-consuming and can suffer
from subjectivity among pathologists. The rise of computational pathology has
led to the development of automated methods for gland segmentation that aim to
overcome the challenges of manual segmentation. However, this task is
non-trivial due to the large variability in glandular appearance and the
difficulty in differentiating between certain glandular and non-glandular
histological structures. Furthermore, a measure of uncertainty is essential for
diagnostic decision making. To address these challenges, we propose a fully
convolutional neural network that counters the loss of information caused by
max-pooling by re-introducing the original image at multiple points within the
network. We also use atrous spatial pyramid pooling with varying dilation rates
for preserving the resolution and multi-level aggregation. To incorporate
uncertainty, we introduce random transformations during test time for an
enhanced segmentation result that simultaneously generates an uncertainty map,
highlighting areas of ambiguity. We show that this map can be used to define a
metric for disregarding predictions with high uncertainty. The proposed network
achieves state-of-the-art performance on the GlaS challenge dataset and on a
second independent colorectal adenocarcinoma dataset. In addition, we perform
gland instance segmentation on whole-slide images from two further datasets to
highlight the generalisability of our method. As an extension, we introduce
MILD-Net+ for simultaneous gland and lumen segmentation, to increase the
diagnostic power of the network.Comment: Initial version published at Medical Imaging with Deep Learning
(MIDL) 201
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis
Histopathological cancer diagnosis is based on visual examination of stained
tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely
employed worldwide. It is easy to acquire and cost effective, but cells and
tissue components show low-contrast with varying tones of dark blue and pink,
which makes difficult visual assessments, digital image analysis, and
quantifications. These limitations can be overcome by IHC staining of target
proteins of the tissue slide. IHC provides a selective, high-contrast imaging
of cells and tissue components, but their use is largely limited by a
significantly more complex laboratory processing and high cost. We proposed a
conditional CycleGAN (cCGAN) network to transform the H\&E stained images into
IHC stained images, facilitating virtual IHC staining on the same slide. This
data-driven method requires only a limited amount of labelled data but will
generate pixel level segmentation results. The proposed cCGAN model improves
the original network \cite{zhu_unpaired_2017} by adding category conditions and
introducing two structural loss functions, which realize a multi-subdomain
translation and improve the translation accuracy as well. % need to give
reasons here. Experiments demonstrate that the proposed model outperforms the
original method in unpaired image translation with multi-subdomains. We also
explore the potential of unpaired images to image translation method applied on
other histology images related tasks with different staining techniques
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