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1. Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model

Author

Zhang, Hongrun, Burrows, Liam, Meng, Yanda, Sculthorpe, Declan, Mukherjee, Abhik, Coupland, Sarah E, Chen, Ke, and Zheng, Yalin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where the target regions are usually with high morphology variations and irregular shapes. Thus, weakly supervised learning with sparse annotations of points is promising to reduce the annotation workload. In this work, we propose a contrast-based variational model to generate segmentation results, which serve as reliable complementary supervision to train a deep segmentation model for histopathology images. The proposed method considers the common characteristics of target regions in histopathology images and can be trained in an end-to-end manner. It can generate more regionally consistent and smoother boundary segmentation, and is more robust to unlabeled `novel' regions. Experiments on two different histology datasets demonstrate its effectiveness and efficiency in comparison to previous models., Comment: Accepted to CVPR2023

Published
2023

2. Using Deep Image Prior to Assist Variational Selective Segmentation Deep Learning Algorithms

Author

Burrows, Liam, Chen, Ke, and Torella, Francesco

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Variational segmentation algorithms require a prior imposed in the form of a regularisation term to enforce smoothness of the solution. Recently, it was shown in the Deep Image Prior work that the explicit regularisation in a model can be removed and replaced by the implicit regularisation captured by the architecture of a neural network. The Deep Image Prior approach is competitive, but is only tailored to one specific image and does not allow us to predict future images. We propose to incorporate the ideas from Deep Image Prior into a more traditional learning algorithm to allow us to use the implicit regularisation offered by the Deep Image Prior, but still be able to predict future images., Comment: Presented at SIPAIM 2021

Published
2021

4. Variational and deep learning methods for selective segmentation

Author

Burrows, Liam

Abstract

Image segmentation aims to decompose an image into segments in order to determine the position of objects in the image. Naturally this has many applications in a wide range of fields, particularly in medical imaging. This thesis is concerned with the development of reliable selective segmentation methods, tackling difficult challenges such as images containing low contrast or images with inhomogeneous intensity. We begin with developing some models in the variational framework only. We propose a new model which assumes a piecewise-smooth intensity distribution, improving on previous selective segmentation works which usually assume a simple piecewise-constant distribution. This improvement allows our model to effectively segment images displaying intensity inhomogeneity with minimal user input. Our proposed model is a convex selective variant of the famous Mumford-Shah model. Another work focused on the variational framework incorporates reproducible kernel Hilbert space (RKHS) methods to improve edge detection in images containing low contrast. We find that, by modelling edges using approximated Heaviside functions (and smooth parts modelled using kernel functions), that edge detection is improved when compared with using the traditional image gradient information. In the second half of this thesis, we begin to incorporate some deep learning methods. We introduce a joint selective segmentation and registration model and investigate how the regularisation offered by the recent Deep Image Prior (DIP) work can benefit our variational model. As well as this, we dedicate a chapter to combining variational segmentation methods with deep learning methods. Deep learning methods, while incredibly popular in recent years, are often restricted by the requirement of large labelled datasets. We demonstrate how labels can be supplemented (or removed entirely) by using a variational method as a loss function using a semi-supervised (or unsupervised) training algorithm. Overall, we show a range of methods to combine both variational and deep learning segmentation methods.

Published
2022
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6. A Deep Image Prior Learning Algorithm for Joint Selective Segmentation and Registration

Author

Burrows, Liam, Chen, Ke, Torella, Francesco, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Elmoataz, Abderrahim, editor, Fadili, Jalal, editor, Quéau, Yvain, editor, Rabin, Julien, editor, and Simon, Loïc, editor

Published
2021
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8. On New Convolutional Neural Network Based Algorithms for Selective Segmentation of Images

Author

Burrows, Liam, Chen, Ke, Torella, Francesco, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Papież, Bartłomiej W., editor, Namburete, Ana I. L., editor, Yaqub, Mohammad, editor, and Noble, J. Alison, editor

Published
2020
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9. Edge Enhancement for Image Segmentation Using a RKHS Method

Author

Burrows, Liam, Guo, Weihong, Chen, Ke, Torella, Francesco, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zheng, Yalin, editor, Williams, Bryan M., editor, and Chen, Ke, editor

Published
2020
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11. A semi-automatic segmentation method for meningioma developed using a variational approach model.

Author

Burrows, Liam, Patel, Jay, Islim, Abdurrahman I, Jenkinson, Michael D, Mills, Samantha J, and Chen, Ke

Abstract

Background: Meningioma is the commonest primary brain tumour. Volumetric post-contrast magnetic resonance imaging (MRI) is recognised as gold standard for delineation of meningioma volume but is hindered by manual processing times. We aimed to investigate the utility of a model-based variational approach in segmenting meningioma. Methods: A database of patients with a meningioma (2007–2015) was queried for patients with a contrast-enhanced volumetric MRI, who had consented to a research tissue biobank. Manual segmentation by a neuroradiologist was performed and results were compared to the mathematical model, using a battery of tests including the Sørensen–Dice coefficient (DICE) and JACCARD index. A publicly available meningioma dataset (708 segmented T1 contrast-enhanced slices) was also used to test the reliability of the model. Results: 49 meningioma cases were included. The most common meningioma location was convexity (n = 15, 30.6%). The mathematical model segmented all but one incidental meningioma, which failed due to the lack of contrast uptake. The median meningioma volume by manual segmentation was 19.0 cm3 (IQR 4.9–31.2). The median meningioma volume using the mathematical model was 16.9 cm3 (IQR 4.6–28.34). The mean DICE score was 0.90 (SD = 0.04). The mean JACCARD index was 0.82 (SD = 0.07). For the publicly available dataset, the mean DICE and JACCARD scores were 0.90 (SD = 0.06) and 0.82 (SD = 0.10), respectively. Conclusions: Segmentation of meningioma volume using the proposed mathematical model was possible with accurate results. Application of this model on contrast-enhanced volumetric imaging may help reduce work burden on neuroradiologists with the increasing number in meningioma diagnoses. [ABSTRACT FROM AUTHOR]

Published
2024
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14. On two convex variational models and their iterative solutions for selective segmentation of images with intensity inhomogeneity.

Author

Burrows, Liam, Ke Chen, and Torella, Francesco

Subjects
IMAGE segmentation, CALCULUS of variations, IMAGE processing, MACHINE learning, INVERSE problems
Abstract

Treating images as functions and using variational calculus, mathematical imaging offers to design novel and continuous methods, outperforming traditional methods based on matrices, for modelling real life tasks in image processing. Image segmentation is one of such fundamental tasks as many application areas demand a reliable segmentation method. Developing reliable selective segmentation algorithms is particularly important in relation to training data preparation in modern machine learning as accurately isolating a specific object in an image with minimal user input is a valuable tool. When an image's intensity is consisted of mainly piecewise constants, convex models are available. Different from previous works, this paper proposes two convex models that are capable of segmenting local features defined by geometric constraints for images having intensity inhomogeneity. Our new, local, selective and convex variants are extended from the non-convex Mumford-Shah model intended for global segmentation. They have fundamentally improved on previous selective models that assume intensity of piecewise constants. Comparisons with related models are conducted to illustrate the advantages of our new models. [ABSTRACT FROM AUTHOR]

Published
2022
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19. Mathematical modelling and deep learning algorithms to automate assessment of single and digitally multiplexed immunohistochemical stains in tumoural stroma.

Author

Burrows L, Sculthorpe D, Zhang H, Rehman O, Mukherjee A, and Chen K

Abstract

Whilst automated analysis of immunostains in pathology research has focused predominantly on the epithelial compartment, automated analysis of stains in the stromal compartment is challenging and therefore requires time-consuming pathological input and guidance to adjust to tissue morphometry as perceived by pathologists. This study aimed to develop a robust method to automate stromal stain analyses using 2 of the commonest stromal stains (SMA and desmin) employed in clinical pathology practice as examples. An effective computational method capable of automatically assessing and quantifying tumour-associated stromal stains was developed and applied on cores of colorectal cancer tissue microarrays. The methodology combines both mathematical models and deep learning techniques with the former requiring no training data and the latter as many inputs as possible. The novel mathematical model was used to produce a digital double marker overlay allowing for fast automated digital multiplex analysis of stromal stains. The results show that deep learning methodologies in combination with mathematical modelling allow for an accurate means of quantifying stromal stains whilst also opening up new possibilities of digital multiplex analyses., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Published
2023
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