27 research outputs found
Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review
Instance segmentation of nuclei and glands in the histology images is an
important step in computational pathology workflow for cancer diagnosis,
treatment planning and survival analysis. With the advent of modern hardware,
the recent availability of large-scale quality public datasets and the
community organized grand challenges have seen a surge in automated methods
focusing on domain specific challenges, which is pivotal for technology
advancements and clinical translation. In this survey, 126 papers illustrating
the AI based methods for nuclei and glands instance segmentation published in
the last five years (2017-2022) are deeply analyzed, the limitations of current
approaches and the open challenges are discussed. Moreover, the potential
future research direction is presented and the contribution of state-of-the-art
methods is summarized. Further, a generalized summary of publicly available
datasets and a detailed insights on the grand challenges illustrating the top
performing methods specific to each challenge is also provided. Besides, we
intended to give the reader current state of existing research and pointers to
the future directions in developing methods that can be used in clinical
practice enabling improved diagnosis, grading, prognosis, and treatment
planning of cancer. To the best of our knowledge, no previous work has reviewed
the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure
Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy
Purpose: Ureteroscopy is an efficient endoscopic minimally invasive technique
for the diagnosis and treatment of upper tract urothelial carcinoma (UTUC).
During ureteroscopy, the automatic segmentation of the hollow lumen is of
primary importance, since it indicates the path that the endoscope should
follow. In order to obtain an accurate segmentation of the hollow lumen, this
paper presents an automatic method based on Convolutional Neural Networks
(CNNs).
Methods: The proposed method is based on an ensemble of 4 parallel CNNs to
simultaneously process single and multi-frame information. Of these, two
architectures are taken as core-models, namely U-Net based in residual
blocks() and Mask-RCNN(), which are fed with single still-frames
. The other two models (, ) are modifications of the former
ones consisting on the addition of a stage which makes use of 3D Convolutions
to process temporal information. , are fed with triplets of frames
(, , ) to produce the segmentation for .
Results: The proposed method was evaluated using a custom dataset of 11
videos (2,673 frames) which were collected and manually annotated from 6
patients. We obtain a Dice similarity coefficient of 0.80, outperforming
previous state-of-the-art methods.
Conclusion: The obtained results show that spatial-temporal information can
be effectively exploited by the ensemble model to improve hollow lumen
segmentation in ureteroscopic images. The method is effective also in presence
of poor visibility, occasional bleeding, or specular reflections
Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment
This paper describes a scalable active learning pipeline prototype for
large-scale brain mapping that leverages high performance computing power. It
enables high-throughput evaluation of algorithm results, which, after human
review, are used for iterative machine learning model training. Image
processing and machine learning are performed in a batch layer. Benchmark
testing of image processing using pMATLAB shows that a 100 increase in
throughput (10,000%) can be achieved while total processing time only increases
by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust
scalability. The images and algorithm results are provided through a serving
layer to a browser-based user interface for interactive review. This pipeline
has the potential to greatly reduce the manual annotation burden and improve
the overall performance of machine learning-based brain mapping.Comment: 6 pages, 5 figures, submitted to IEEE HPEC 2020 proceeding
CPP-Net: Context-aware Polygon Proposal Network for Nucleus Segmentation
Nucleus segmentation is a challenging task due to the crowded distribution
and blurry boundaries of nuclei. Recent approaches represent nuclei by means of
polygons to differentiate between touching and overlapping nuclei and have
accordingly achieved promising performance. Each polygon is represented by a
set of centroid-to-boundary distances, which are in turn predicted by features
of the centroid pixel for a single nucleus. However, using the centroid pixel
alone does not provide sufficient contextual information for robust prediction.
To handle this problem, we propose a Context-aware Polygon Proposal Network
(CPP-Net) for nucleus segmentation. First, we sample a point set rather than
one single pixel within each cell for distance prediction. This strategy
substantially enhances contextual information and thereby improves the
robustness of the prediction. Second, we propose a Confidence-based Weighting
Module, which adaptively fuses the predictions from the sampled point set.
Third, we introduce a novel Shape-Aware Perceptual (SAP) loss that constrains
the shape of the predicted polygons. Here, the SAP loss is based on an
additional network that is pre-trained by means of mapping the centroid
probability map and the pixel-to-boundary distance maps to a different nucleus
representation. Extensive experiments justify the effectiveness of each
component in the proposed CPP-Net. Finally, CPP-Net is found to achieve
state-of-the-art performance on three publicly available databases, namely
DSB2018, BBBC06, and PanNuke. Code of this paper will be released
Detection and segmentation of macrophages in Quantitative Phase Images by Deep Learning using a Mask Region-based Convolutional Neural
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceQuantitative Phase Imaging (QPI) has been demonstrated to be a versatile tool for minimally invasive
label-free imaging of biological specimens and time-resolved cellular analysis. RAW 264.7 mouse
macrophages were imaged by Digital Holographic Microscopy (DHM), an interferometry-based variant
of QPI, in toxicological studies and cellular growth experiments. Robust detection and segmentation
of cells in QPI images by Deep Learning facilitates automated data evaluation of images in high
throughput microscopy. Detection, segmentation and the subsequent analysis of single cellular
specimens in QPI images yields essential toxicity related physical parameters like the dry mass on the
single-cell level. Deep Learning models, such as the Mask Region-based Convolutional Neural Network
(Mask R-CNN), were proven to achieve robust results for object detection in fluorescence microscopy
images. Thus, a Mask R-CNN was applied with the aim to obtain deeper cellular knowledge from DHM
QPI images. This work shows that the combination of label-free DHM and a state-of-the-art Deep
Learning model achieves reliable machine-generated data on the single-cell level and prospects to
enhance the information as well as the quality of physical data that can be extracted from QPI images
of biomedical experiments and label-free high throughput microscopy
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in
helping us further understand the relationship between histology and patient
outcome. To drive innovation in this area, we setup a community-wide challenge
using the largest available dataset of its kind to assess nuclear segmentation
and cellular composition. Our challenge, named CoNIC, stimulated the
development of reproducible algorithms for cellular recognition with real-time
result inspection on public leaderboards. We conducted an extensive
post-challenge analysis based on the top-performing models using 1,658
whole-slide images of colon tissue. With around 700 million detected nuclei per
model, associated features were used for dysplasia grading and survival
analysis, where we demonstrated that the challenge's improvement over the
previous state-of-the-art led to significant boosts in downstream performance.
Our findings also suggest that eosinophils and neutrophils play an important
role in the tumour microevironment. We release challenge models and WSI-level
results to foster the development of further methods for biomarker discovery