1,275 research outputs found
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Automated classification of histopathological whole-slide images (WSI) of
breast tissue requires analysis at very high resolutions with a large
contextual area. In this paper, we present context-aware stacked convolutional
neural networks (CNN) for classification of breast WSIs into normal/benign,
ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first
train a CNN using high pixel resolution patches to capture cellular level
information. The feature responses generated by this model are then fed as
input to a second CNN, stacked on top of the first. Training of this stacked
architecture with large input patches enables learning of fine-grained
(cellular) details and global interdependence of tissue structures. Our system
is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast
tissue specimens. The system achieves an AUC of 0.962 for the binary
classification of non-malignant and malignant slides and obtains a three class
accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC,
demonstrating its potentials for routine diagnostics
Digital pathology in clinical use: where are we now and what is holding us back?
Whole slide imaging is being used increasingly in research applications and in frozen section, consultation and external quality assurance practice. Digital pathology, when integrated with other digital tools such as barcoding, specimen tracking and digital dictation, can be integrated into the histopathology workflow, from specimen accession to report sign-out. These elements can bring about improvements in the safety, quality and efficiency of a histopathology department. The present paper reviews the evidence for these benefits. We then discuss the challenges of implementing a fully digital pathology workflow, including the regulatory environment, validation of whole slide imaging and the evidence for the design of a digital pathology workstation
Automated analysis of colorectal cancer
Colorectal cancer (CRC) is the second largest cause of cancer deaths in the UK, with approximately 16,000 per year. Over 41,000 people are diagnosed annually, and 43% of those will die within ten years of diagnosis. The treatment of CRC patients relies on pathological examination of the disease to identify visual features that predict growth and spread, and response to chemoradiotherapy. These prognostic features are identified manually, and are subject to inter and intra-scorer variability. This variability stems from the subjectivity in interpreting large images which can have very varied appearances, as well as the time consuming and laborious methodology of visually inspecting cancer cells.
The work in this thesis presents a systematic approach to developing a solution to address this problem for one such prognostic indicator, the Tumour:Stroma Ratio (TSR). The steps taken are presented sequentially through the chapters, in order of the work carried out. These specifically involve the acquisition and assessment of a dataset of 2.4 million expert-classified images of CRC, and multiple iterations of algorithm development, to automate the process of generating TSRs for patient cases. The algorithm improvements are made using conclusions from observer studies, conducted on a psychophysics experiment platform developed as part of this work, and further work is undertaken to identify issues of image quality that affect automated solutions. The developed algorithm is then applied to a clinical trial dataset with survival data, meaning that the algorithm is validated against two separate pathologist-scored, clinical trial datasets, as well as being able to test its suitability for generating independent prognostic markers
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
Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer
Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low-and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach
Capturing Global Spatial Context for Accurate Cell Classification in Skin Cancer Histology
The spectacular response observed in clinical trials of immunotherapy in
patients with previously uncurable Melanoma, a highly aggressive form of skin
cancer, calls for a better understanding of the cancer-immune interface.
Computational pathology provides a unique opportunity to spatially dissect such
interface on digitised pathological slides. Accurate cellular classification is
a key to ensure meaningful results, but is often challenging even with
state-of-art machine learning and deep learning methods.
We propose a hierarchical framework, which mirrors the way pathologists
perceive tumour architecture and define tumour heterogeneity to improve cell
classification methods that rely solely on cell nuclei morphology. The SLIC
superpixel algorithm was used to segment and classify tumour regions in low
resolution H&E-stained histological images of melanoma skin cancer to provide a
global context. Classification of superpixels into tumour, stroma, epidermis
and lumen/white space, yielded a 97.7% training set accuracy and 95.7% testing
set accuracy in 58 whole-tumour images of the TCGA melanoma dataset. The
superpixel classification was projected down to high resolution images to
enhance the performance of a single cell classifier, based on cell nuclear
morphological features, and resulted in increasing its accuracy from 86.4% to
91.6%. Furthermore, a voting scheme was proposed to use global context as
biological a priori knowledge, pushing the accuracy further to 92.8%.
This study demonstrates how using the global spatial context can accurately
characterise the tumour microenvironment and allow us to extend significantly
beyond single-cell morphological classification.Comment: Accepted by MICCAI COMPAY 2018 worksho
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