28 research outputs found
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images
Medical image segmentation assists in computer-aided diagnosis, surgeries,
and treatment. Digitize tissue slide images are used to analyze and segment
glands, nuclei, and other biomarkers which are further used in computer-aided
medical applications. To this end, many researchers developed different neural
networks to perform segmentation on histological images, mostly these networks
are based on encoder-decoder architecture and also utilize complex attention
modules or transformers. However, these networks are less accurate to capture
relevant local and global features with accurate boundary detection at multiple
scales, therefore, we proposed an Encoder-Decoder Network, Quick Attention
Module and a Multi Loss Function (combination of Binary Cross Entropy (BCE)
Loss, Focal Loss & Dice Loss). We evaluate the generalization capability of our
proposed network on two publicly available datasets for medical image
segmentation MoNuSeg and GlaS and outperform the state-of-the-art networks with
1.99% improvement on the MoNuSeg dataset and 7.15% improvement on the GlaS
dataset. Implementation Code is available at this link: https://bit.ly/HistoSegComment: Accepted by 2022 12th International Conference on Pattern Recognition
Systems (ICPRS), For Implementation Code see https://bit.ly/HistoSe
Exploiting peer group concept for adaptive and highly available services
This paper presents a prototype for redundant, highly available and fault
tolerant peer to peer framework for data management. Peer to peer computing is
gaining importance due to its flexible organization, lack of central authority,
distribution of functionality to participating nodes and ability to utilize
unused computational resources. Emergence of GRID computing has provided much
needed infrastructure and administrative domain for peer to peer computing. The
components of this framework exploit peer group concept to scope service and
information search, arrange services and information in a coherent manner,
provide selective redundancy and ensure availability in face of failure and
high load conditions. A prototype system has been implemented using JXTA peer
to peer technology and XML is used for service description and interfaces,
allowing peers to communicate with services implemented in various platforms
including web services and JINI services. It utilizes code mobility to achieve
role interchange among services and ensure dynamic group membership. Security
is ensured by using Public Key Infrastructure (PKI) to implement group level
security policies for membership and service access.Comment: The Paper Consists of 5 pages, 6 figures submitted in Computing in
High Energy and Nuclear Physics, 24-28 March 2003 La Jolla California. CHEP0
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
Optimizing the trainable B-COSFIRE filter for retinal blood vessel segmentation
Segmentation of the retinal blood vessels using filtering techniques is a widely used step in the development of an automated system for diagnostic retinal image analysis. This paper optimized the blood vessel segmentation, by extending the trainable B-COSFIRE filter via identification of more optimal parameters. The filter parameters are introduced using an optimization procedure to three public datasets (STARE, DRIVE, and CHASE-DB1). The suggested approach considers analyzing thresholding parameters selection followed by application of background artifacts removal techniques. The approach results are better than the other state of the art methods used for vessel segmentation. ANOVA analysis technique is also used to identify the most significant parameters that are impacting the performance results (p-value ¡ 0.05). The proposed enhancement has improved the vessel segmentation accuracy in DRIVE, STARE and CHASE-DB1 to 95.47, 95.30 and 95.30, respectively
Context-aware convolutional neural network for grading of colorectal cancer histology images
Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224 × 224) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate a larger context by a context-aware neural network based on images with a dimension of 1792 × 1792 pixels. The proposed framework first encodes the local representation of a histology image into high dimensional features then aggregates the features by considering their spatial organization to make a final prediction. We evaluated the proposed method on two colorectal cancer datasets for the task of cancer grading. Our method outperformed the traditional patch-based approaches, problem-specific methods, and existing context-based methods. We also presented a comprehensive analysis of different variants of the proposed method
CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023
Quantitative cephalometric analysis is the most widely used clinical and
research tool in modern orthodontics. Accurate localization of cephalometric
landmarks enables the quantification and classification of anatomical
abnormalities, however, the traditional manual way of marking these landmarks
is a very tedious job. Endeavours have constantly been made to develop
automated cephalometric landmark detection systems but they are inadequate for
orthodontic applications. The fundamental reason for this is that the amount of
publicly available datasets as well as the images provided for training in
these datasets are insufficient for an AI model to perform well. To facilitate
the development of robust AI solutions for morphometric analysis, we organise
the CEPHA29 Automatic Cephalometric Landmark Detection Challenge in conjunction
with IEEE International Symposium on Biomedical Imaging (ISBI 2023). In this
context, we provide the largest known publicly available dataset, consisting of
1000 cephalometric X-ray images. We hope that our challenge will not only
derive forward research and innovation in automatic cephalometric landmark
identification but will also signal the beginning of a new era in the
discipline
'Aariz: A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification
The accurate identification and precise localization of cephalometric
landmarks enable the classification and quantification of anatomical
abnormalities. The traditional way of marking cephalometric landmarks on
lateral cephalograms is a monotonous and time-consuming job. Endeavours to
develop automated landmark detection systems have persistently been made,
however, they are inadequate for orthodontic applications due to unavailability
of a reliable dataset. We proposed a new state-of-the-art dataset to facilitate
the development of robust AI solutions for quantitative morphometric analysis.
The dataset includes 1000 lateral cephalometric radiographs (LCRs) obtained
from 7 different radiographic imaging devices with varying resolutions, making
it the most diverse and comprehensive cephalometric dataset to date. The
clinical experts of our team meticulously annotated each radiograph with 29
cephalometric landmarks, including the most significant soft tissue landmarks
ever marked in any publicly available dataset. Additionally, our experts also
labelled the cervical vertebral maturation (CVM) stage of the patient in a
radiograph, making this dataset the first standard resource for CVM
classification. We believe that this dataset will be instrumental in the
development of reliable automated landmark detection frameworks for use in
orthodontics and beyond