Neil A. Thacker’s research while affiliated with University of Manchester and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (302)


Figure 1: The distribution of the median DCE MRI variables K trans , v e , v p and 100(1 − E f rac ).
Figure 2: Bland-Altman plots, showing reproducibility for median values of k trans , v p , v e and E f rac . A clear parameter dependant accuracy is seen for all variables.
Figure 5: The distributions of statistical distances, D tran , for within subjects (left) and between subject (right) tumours. Computed using a chi squared statistic based on the transformed and scaled DCE MRI summary variables of median K trans ,v e ,v p and 100(1 − E f rac )).
Reliability of Tumour Classification from Multi-Dimensional DCE-MRI Variables using Data Transformations
  • Preprint
  • File available

March 2023

·

36 Reads

S. V. Notley

·

N. A. Thacker

·

L. Horsley

·

[...]

·

mean DCE-MRI variables show a clear dependency between signal and noise variance, which can be shown to reduce the effectiveness of difference assessments. Appropriate transformation of these variables supports statistically efficient and robust comparisons. The capabilities of DCE-MRI based descriptions of hepatic colorectal tumour classification was assessed, with regard to their potential for use as imaging biomarkers. Four DCE-MRI parameters were extracted from 102 selected tumour regions. A multi-dimensional statistical distance metric was assessed for the challenging task of comparing intra- and inter- subject tumour differences. Statistical errors were estimated using bootstrap resampling. The potential for tumour classification was assessed via Monte Carlo simulation. Transformation of the variables and fusion into a single chi-squared statistic shows that inter subject variation in hepatic tumours is measurable and significantly greater than intra-subject variation at the group level. However, reliability analysis shows that, at current noise levels, individual tumour assessment is not possible. Appropriate data transforms for DCE-MRI derived parameters produce an improvement in statistical sensitivity compared to conventional approaches. Reliability analysis shows, that even with data transformation, DCI-MRI variables do not currently facilitate good tumour discrimination and a doubling of SNR is needed to support non-trivial levels of classification

Download

Synchrotron Imaging Derived Relationship between Process Parameters and Build Quality for Directed Energy Deposition Additively Manufactured IN718

March 2023

·

48 Reads

·

5 Citations

Additive Manufacturing Letters

Laser additive manufacturing is transforming several industrial sectors, especially the directed energy deposition process. A key challenge in the widespread uptake of this emerging technology is the formation of undesirable microstructural features such as pores, cracks, and large epitaxial grains. The trial and error approach to establish the relationship between process parameters and material properties is problematic due to the transient nature of the process and the number of parameters involved. In this work, the relationship between process parameters, melt pool geometry and quality of build measures, using directed energy deposition additive manufacturing for IN718, is quantified using neural networks as generalised regressors in a statistically robust manner. The data was acquired using in-situ synchrotron x-ray imaging providing unique and accurate measurements for our analysis. An analysis of the variations across repeated measurements show heteroscedastic error characteristics that are accounted for using a principled nonlinear data transformation method. The results of the analysis show that surface roughness correlates with melt pool geometry while the track height directly correlates with process parameters indicating a potential to directly control efficiency and layer thickness while independently minimising surface roughness.


Figure 1: Example spatial distributions of ADC values in selected tumors. Visually, HCT116 tumors are more complex and variable than LoVo tumors.
Figure 4: Estimated components (PMFS: P (ADC, t|C) and P (ADC, t|T )), one color per component. Left and right plots indicate baseline and 72 hours. Top: LoVo control components. Bottom: LoVo treatment components
Fig. 14. ICA lamb spectra component 7
HCT116 control cohort result significances. Main figures show results for leave-all-in analysis. Figures in brackets show leave-one-out results, where the model was trained on all except the current tumor before being applied to the current tumor.
A New Method for the High-Precision Assessment of Tumor Changes in Response to Treatment

September 2022

·

48 Reads

Imaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both normal development and also in response to treatment. The large variations observed in control group tumors can obscure detection of significant therapeutic effects due to the ambiguity in attributing causes of change. This can hinder development of effective therapies due to limitations in experimental design, rather than due to therapeutic failure. An improved method to model biological variation and heterogeneity in imaging signals is described. Specifically, Linear Poisson modelling (LPM) evaluates changes in apparent diffusion co-efficient (ADC) before and 72 hours after radiotherapy, in two xenograft models of colorectal cancer. The statistical significance of measured changes are compared to those attainable using a conventional t-test analysis on basic ADC distribution parameters. When LPMs were applied to treated tumors, the LPMs detected highly significant changes. The analyses were significant for all tumors, equating to a gain in power of 4 fold (i.e. equivelent to having a sample size 16 times larger), compared with the conventional approach. In contrast, highly significant changes are only detected at a cohort level using t-tests, restricting their potential use within personalised medicine and increasing the number of animals required during testing. Furthermore, LPM enabled the relative volumes of responding and non-responding tissue to be estimated for each xenograft model. Leave-one-out analysis of the treated xenografts provided quality control and identified potential outliers, raising confidence in LPM data at clinically relevant sample sizes.


Understanding and Reducing Crater Counting Errors in Citizen Science Data and the Need for Standardisation

September 2022

·

32 Reads

Citizen science has become a popular tool for preliminary data processing tasks, such as identifying and counting Lunar impact craters in modern high-resolution imagery. However, use of such data requires that citizen science products are understandable and reliable. Contamination and missing data can reduce the usefulness of datasets so it is important that such effects are quantified. This paper presents a method, based upon a newly developed quantitative pattern recognition system (Linear Poisson Models) for estimating levels of contamination within MoonZoo citizen science crater data. Evidence will show that it is possible to remove the effects of contamination, with reference to some agreed upon ground truth, resulting in estimated crater counts which are highly repeatable. However, it will also be shown that correcting for missing data is currently more difficult to achieve. The techniques are tested on MoonZoo citizen science crater annotations from the Apollo 17 site and also undergraduate and expert results from the same region.


Habitat Imaging of Tumors Enables High Confidence Sub-Regional Assessment of Response to Therapy

April 2022

·

71 Reads

·

6 Citations

Imaging biomarkers are used in therapy development to identify and quantify therapeutic response. In oncology, use of MRI, PET and other imaging methods can be complicated by spatially complex and heterogeneous tumor micro-environments, non-Gaussian data and small sample sizes. Linear Poisson Modelling (LPM) enables analysis of complex data that is quantitative and can operate in small data domains. We performed experiments in 5 mouse models to evaluate the ability of LPM to identify responding tumor habitats across a range of radiation and targeted drug therapies. We tested if LPM could identify differential biological response rates. We calculated the theoretical sample size constraints for applying LPM to new data. We then performed a co-clinical trial using small data to test if LPM could detect multiple therapeutics with both improved power and reduced animal numbers compared to conventional t-test approaches. Our data showed that LPM greatly increased the amount of information extracted from diffusion-weighted imaging, compared to cohort t-tests. LPM distinguished biological response rates between Calu6 tumors treated with 3 different therapies and between Calu6 tumors and 4 other xenograft models treated with radiotherapy. A simulated co-clinical trial using real data detected high precision per-tumor treatment effects in as few as 3 mice per cohort, with p-values as low as 1 in 10,000. These findings provide a route to simultaneously improve the information derived from preclinical imaging while reducing and refining the use of animals in cancer research.


Fig. 1. Outline of work-flow. Numbers indicate related method sub-sections
Fig. 4. Hypothesis testing in simulation. Top row: v 2 full shows spatial map of v 2 values when all LPM components are used to describe the data; v 2 path
Fig. 5. Left: model selection curve showing goodness-of-fit (v 2 D ) as a function of model-order N for rat brain image. Right: Bland-Altman analysis of MALDI MS, as corroborated in earlier work (Deepaisarn et al., 2018)
A reformulation of pLSA for uncertainty estimation and hypothesis testing in bio-imaging

April 2020

·

37 Reads

·

3 Citations

Bioinformatics

Motivation: Probabilistic Latent Semantic Analysis (pLSA) is commonly applied to describe mass spectra (MS) images. However, the method does not provide certain outputs necessary for the quantitative scientific interpretation of data. In particular, it lacks assessment of statistical uncertainty and the ability to perform hypothesis testing. We show how Linear Poisson Modelling (LPM) advances pLSA, giving covariances on model parameters and supporting χ2 testing for the presence/absence of MS signal components. As an example, this is useful for the identification of pathology in MALDI biological samples. We also show potential wider applicability, beyond mass spectra, using MRI data from colorectal xenograft models. Results: Simulations and MALDI spectra of a stroke-damaged rat brain show MS signals from pathological tissue can be quantified. MRI diffusion data of control and radiotherapy-treated tumors further show high sensitivity hypothesis testing for treatment effects. Successful χ2 and degrees-of-freedom are computed, allowing null hypothesis thresholding at high levels of confidence. Availability: Open source image analysis software available from TINA Vision, www.tina-vision.net. Supplementary information: Supplementary material is available at Bioinformatics online.


Fundamental Issues Regarding Uncertainties in Artificial Neural Networks

February 2020

·

23 Reads

Artificial Neural Networks (ANNs) implement a specific form of multi-variate extrapolation and will generate an output for any input pattern, even when there is no similar training pattern. Extrapolations are not necessarily to be trusted, and in order to support safety critical systems, we require such systems to give an indication of the training sample related uncertainty associated with their output. Some readers may think that this is a well known issue which is already covered by the basic principles of pattern recognition. We will explain below how this is not the case and how the conventional (Likelihood estimate of) conditional probability of classification does not correctly assess this uncertainty. We provide a discussion of the standard interpretations of this problem and show how a quantitative approach based upon long standing methods can be practically applied. The methods are illustrated on the task of early diagnosis of dementing diseases using Magnetic Resonance Imaging.





Citations (63)


... Aunque el proceso DED es ampliamente utilizado en la fabricación, requiere un enfriamiento adecuado entre capas para evitar fallas geométricas causadas por el sobrecalentamiento de la sección media (Hwang et al., 2023). La fabricación aditiva por láser ha transformado varios sectores industriales, especialmente en el proceso de deposición de energía dirigida, con un potencial para controlar la eficiencia y el grosor de la capa, minimizando la rugosidad de la superficie de los materiales (Ehmsen et al., 2023;Ikeda et al., 2023;Notley et al., 2023;Sargent et al., 2023). ...

Reference:

La manufactura aditiva como elemento imprescindible de la industria 4.0 en beneficio de la ingeniería: un análisis bibliométrico
Synchrotron Imaging Derived Relationship between Process Parameters and Build Quality for Directed Energy Deposition Additively Manufactured IN718
  • Citing Article
  • March 2023

Additive Manufacturing Letters

... In comparison to image-based methods for habitat generation, (11, 12, 21, and 22) created by intersections between T1 habitat map and T2 habitat map for the final habitat map; (B) The scatter plot (left) and map (right) share the same color map to identify the 3 clusters using K-mean algorithm this technique demonstrates qualitative consistency with pathological observations. Additionally, Tar et al. [32] utilized habitat imaging to detect tumor response to drug and radiation therapy in an innovative way. With the aid of the linear Poisson modeling, their approach significantly reduced the number of animals needed for multiple therapeutic interventions, thereby enhancing information acquisition from preclinical imaging. ...

Habitat Imaging of Tumors Enables High Confidence Sub-Regional Assessment of Response to Therapy

... ADC CoV , a coefficient that increases ADC strength by minimizing tumor heterogeneity, was calculated by dividing the ADC standard deviation by the mean ADC and multiplying the result by 100. 14 ...

Considering tumour volume for motion corrected DWI of colorectal liver metastases increases sensitivity of ADC to detect treatment-induced changes

... Baseline correction and relative mass calibration is required to mitigate against non-Poisson effects. Software developed in Thacker et al. (2018) was used to perform a Fourier domain peak alignment, estimate and remove a smooth background and identify and integrate bins with significant peaks. Peak identification is performed on the average total spectrum from all spectra after alignment. ...

The Statistical Properties of Raw and Preprocessed ToF Mass Spectra
  • Citing Article
  • March 2018

International Journal of Mass Spectrometry

... Previously, we presented Linear Poisson Modelling (LPM) to analyse histograms in physical [11] and biological [12,13] sciences. In this latter study, LPM identified responding volumes of tumors treated with RT in xenograft models of colorectal cancer. ...

A new method for the high-precision assessment of tumor changes in response to treatment

Bioinformatics

... Absolute quantification is straightforward if the extraction efficiency is known [74], although, it depends on several factors including the thickness and type of tissue section, chemical properties of the solvent used for extraction and the analyte, the scan rate and the spatial resolution used for the experiment. Advanced data processing has also been proposed to mitigate the main issue of signal variability caused by matrix effects and extraction efficiency in quantification studies [75]. ...

Quantifying Biological Samples using Linear Poisson Independent Component Analysis for MALDI-ToF Mass Spectra

Bioinformatics

... However, a recent study on bone marrow using T1WI and T2WI in a multi-MRI-scanner test-retest scenario indicated that only a limited number of radiomics features are reproducible with different MRI sequences or scanners [19]; this might raise concerns for the generalizability of single-scanner studies; however, it's important to note that the sequences and imaging locations in this study [19] differ from those in our research. In addition, although some prior studies have shown that ADC exhibits good repeatability across different institutions and MRI vendors [20,21], some other studies on bone marrow and prostate [22,23], have shown significant variability in ADC reproducibility. These contrasting findings highlight the intricacies of ADC analysis and suggest that factors such as different anatomic locations or tissues [24] and the measurement methods (2D or 3D) may contribute significantly to the variability of ADC reproducibility. ...

A data-driven statistical model that estimates measurement uncertainty improves interpretation of ADC reproducibility: A multi-site study of liver metastases

... Examples include describing circumstellar debris disk candidates (Nguyen et al., 2018); classification of protein localization patterns in microscopy images (Sullivan et al., 2018); and morphological classification of galaxies (Jiménez et al., 2020;Kuminski et al., 2014;Shamir et al., 2016). Lastly, the projects benefiting from citizen scientists identifying and counting objects asked citizens to identify and locate animals of particular species (Bowley et al., 2019;Torney et al., 2019); mark potential archeological sites (Lambers et al., 2019), and identify and locate Moon craters (Tar et al., 2017), and interstellar bubbles on images (Beaumont et al., 2014;Duo and Offner 2017). Kim et al. (2014) reported on the EyeWire game project, where players contribute to mapping 3D structures of retinal neurons by coloring the area that belongs to one neuron and avoiding coloring other neurons on a 2D slice image. ...

Estimating False Positive Contamination in Crater Annotations from Citizen Science Data

Earth Moon and Planets

... While the above-mentioned publications [5,7] focused on achievable resolution in spectroscopy and MRI of a point source, the focus of this work was the achievable precision of localization of object boundaries. Many medical applications of MRI have to deal with segmentation of images in order to extract boundaries between objects, and numerous studies have been performed in order to test different algo- rithms121314151617. While being very useful for the considered medical applications, the majority of the studies did not use theoretical criteria to assess the performance of the proposed algorithms. ...

Robust tissue boundary detection for cerebral cortical thickness estimation
  • Citing Conference Paper
  • January 2005

Lecture Notes in Computer Science

... Often unexperienced nonprofessionals are paid to generate the ground truth data only after a brief instruction. [29][30][31][32][33] Here we propose an innovative solution to the problem. IsletSwipe can be operated by real experts during brief idle time periods (e.g. ...

The Moon Zoo citizen science project: Preliminary results for the Apollo 17 landing site

Icarus