368 research outputs found
Using texture analysis in the development of a potential radiomic signature for early identification of hepatic metastasis in colorectal cancer
Background: Radiomics allows information not readily available to the naked eye to be extracted from high resolution imaging modalities such as CT. Identifying that a cancer has already metastasised at the time of presentation through a radiomic signature will affect the treatment pathway. The ability to recognise the existence of metastases earlier will have a significant impact on the survival outcomes. / Aim: To create a novel radiomic signature using textural analysis in the evaluation of synchronous liver metastases in colorectal cancer. / Methods: CT images at baseline and subsequent surveillance over a 5-year period of patients with colorectal cancer were processed using textural analysis software. Comparison was made between those patients who developed liver metastases and those that remained disease free to detect differences in the ‘texture’ of the liver. / Results: A total of 24 patients were divided into two matched groups for comparison. Significant differences between the two groups scores when using the textural analysis programme were found on coarse filtration (p = 0.044). Patients that went on to develop metastases an average of 18 months after presentation had higher levels of hepatic heterogeneity on CT. / Conclusion: This initial study demonstrates the potential of using a textural analysis programme to build a radiomic signature to predict the development of hepatic metastases in rectal cancer patients otherwise thought to have clear staging CT scans at time of presentation
Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images
Background: Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent type of pancreas cancer with a high mortality rate and its staging is highly dependent on the extent of involvement between the tumor and surrounding vessels, facilitating treatment response assessment in PDAC. Objective: This study aims at detecting and visualizing the tumor region and the surrounding vessels in PDAC CT scan since, despite the tumors in other abdominal organs, clear detection of PDAC is highly difficult. Material and Methods: This retrospective study consists of three stages: 1) a patch-based algorithm for differentiation between tumor region and healthy tissue using multi-scale texture analysis along with L1-SVM (Support Vector Machine) classifier, 2) a voting-based approach, developed on a standard logistic function, to mitigate false detections, and 3) 3D visualization of the tumor and the surrounding vessels using ITK-SNAP software. Results: The results demonstrate that multi-scale texture analysis strikes a balance between recall and precision in tumor and healthy tissue differentiation with an overall accuracy of 0.78±0.12 and a sensitivity of 0.90±0.09 in PDAC. Conclusion: Multi-scale texture analysis using statistical and wavelet-based features along with L1-SVM can be employed to differentiate between healthy and pancreatic tissues. Besides, 3D visualization of the tumor region and surrounding vessels can facilitate the assessment of treatment response in PDAC. However, the 3D visualization software must be further developed for integrating with clinical applications
Artificial Intelligence for Digital and Computational Pathology
Advances in digitizing tissue slides and the fast-paced progress in
artificial intelligence, including deep learning, have boosted the field of
computational pathology. This field holds tremendous potential to automate
clinical diagnosis, predict patient prognosis and response to therapy, and
discover new morphological biomarkers from tissue images. Some of these
artificial intelligence-based systems are now getting approved to assist
clinical diagnosis; however, technical barriers remain for their widespread
clinical adoption and integration as a research tool. This Review consolidates
recent methodological advances in computational pathology for predicting
clinical end points in whole-slide images and highlights how these developments
enable the automation of clinical practice and the discovery of new biomarkers.
We then provide future perspectives as the field expands into a broader range
of clinical and research tasks with increasingly diverse modalities of clinical
data
Segmentation and Deformable Modelling Techniques for a Virtual Reality Surgical Simulator in Hepatic Oncology
Liver surgical resection is one of the most frequently used curative therapies. However,
resectability is problematic. There is a need for a computer-assisted surgical planning and
simulation system which can accurately and efficiently simulate the liver, vessels and
tumours in actual patients. The present project describes the development of these core
segmentation and deformable modelling techniques.
For precise detection of irregularly shaped areas with indistinct boundaries, the
segmentation incorporated active contours - gradient vector flow (GVF) snakes and level sets.
To improve efficiency, a chessboard distance transform was used to replace part of the GVF
effort. To automatically initialize the liver volume detection process, a rotating template was
introduced to locate the starting slice. For shape maintenance during the segmentation
process, a simplified object shape learning step was introduced to avoid occasional
significant errors. Skeletonization with fuzzy connectedness was used for vessel
segmentation.
To achieve real-time interactivity, the deformation regime of this system was based
on a single-organ mass-spring system (MSS), which introduced an on-the-fly local mesh
refinement to raise the deformation accuracy and the mesh control quality. This method was
now extended to a multiple soft-tissue constraint system, by supplementing it with an
adaptive constraint mesh generation. A mesh quality measure was tailored based on a wide
comparison of classic measures. Adjustable feature and parameter settings were thus
provided, to make tissues of interest distinct from adjacent structures, keeping the mesh
suitable for on-line topological transformation and deformation.
More than 20 actual patient CT and 2 magnetic resonance imaging (MRI) liver
datasets were tested to evaluate the performance of the segmentation method. Instrument
manipulations of probing, grasping, and simple cutting were successfully simulated on
deformable constraint liver tissue models. This project was implemented in conjunction with
the Division of Surgery, Hammersmith Hospital, London; the preliminary reality effect was
judged satisfactory by the consultant hepatic surgeon
Deep Domain Adaptation Learning Framework for Associating Image Features to Tumour Gene Profile
While medical imaging and general pathology are routine in cancer diagnosis, genetic sequencing is not always assessable due to the strong phenotypic and genetic heterogeneity of human cancers. Image-genomics integrates medical imaging and genetics to provide a complementary approach to optimise cancer diagnosis by associating tumour imaging traits with clinical data and has demonstrated its potential in identifying imaging surrogates for tumour biomarkers. However, existing image-genomics research has focused on quantifying tumour visual traits according to human understanding, which may not be optimal across different cancer types. The challenge hence lies in the extraction of optimised imaging representations in an objective data-driven manner. Such an approach requires large volumes of annotated image data that are difficult to acquire. We propose a deep domain adaptation learning framework for associating image features to tumour genetic information, exploiting the ability of domain adaptation technique to learn relevant image features from close knowledge domains. Our proposed framework leverages the current state-of-the-art in image object recognition to provide image features to encode subtle variations of tumour phenotypic characteristics with domain adaptation techniques. The proposed framework was evaluated with current state-of-the-art in: (i) tumour histopathology image classification and; (ii) image-genomics associations. The proposed framework demonstrated improved accuracy of tumour classification, as well as providing additional data-derived representations of tumour phenotypic characteristics that exhibit strong image-genomics association. This thesis advances and indicates the potential of image-genomics research to reveal additional imaging surrogates to genetic biomarkers, which has the potential to facilitate cancer diagnosis
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
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