Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Deep learning for texture and dynamic texture analysis

Texture is a fundamental visual cue in computer vision which provides useful information about image regions. Dynamic Texture (DT) extends the analysis of texture to sequences of moving scenes. Classic approaches to texture and DT analysis are based on shallow hand-crafted descriptors including local binary patterns and filter banks. Deep learning and in particular Convolutional Neural Networks (CNNs) have significantly contributed to the field of computer vision in the last decade. These biologically inspired networks trained with powerful algorithms have largely improved the state of the art in various tasks such as digit, object and face recognition. This thesis explores the use of CNNs in texture and DT analysis, replacing classic hand-crafted filters by deep trainable filters. An introduction to deep learning is provided in the thesis as well as a thorough review of texture and DT analysis methods. While CNNs present interesting features for the analysis of textures such as a dense extraction of filter responses trained end to end, the deepest layers used in the decision rules commonly learn to detect large shapes and image layout instead of local texture patterns. A CNN architecture is therefore adapted to textures by using an orderless pooling of intermediate layers to discard the overall shape analysis, resulting in a reduced computational cost and improved accuracy. An application to biomedical texture images is proposed in which large tissue images are tiled and combined in a recognition scheme. An approach is also proposed for DT recognition using the developed CNNs on three orthogonal planes to combine spatial and temporal analysis. Finally, a fully convolutional network is adapted to texture segmentation based on the same idea of discarding the overall shape and by combining local shallow features with larger and deeper features

Better prognostic markers for nonmuscle invasive papillary urothelial carcinomas

Bladder cancer is a common type of cancer, especially among men in developed countries. Most cancers in the urinary bladder are papillary urothelial carcinomas. They are characterized by a high recurrence frequency (up to 70 %) after local resection. It is crucial for prognosis to discover these recurrent tumours at an early stage, especially before they become muscle-invasive. Reliable prognostic biomarkers for tumour recurrence and stage progression are lacking. This is why patients diagnosed with a non-muscle invasive bladder cancer follow extensive follow-up regimens with possible serious side effects and with high costs for the healthcare systems. WHO grade and tumour stage are two central biomarkers currently having great impact on both treatment decisions and follow-up regimens. However, there are concerns regarding the reproducibility of WHO grading, and stage classification is challenging in small and fragmented tumour material. In Paper I, we examined the reproducibility and the prognostic value of all the individual microscopic features making up the WHO grading system. Among thirteen extracted features there was considerable variation in both reproducibility and prognostic value. The only feature being both reasonably reproducible and statistically significant prognostic was cell polarity. We concluded that further validation studies are needed on these features, and that future grading systems should be based on well-defined features with true prognostic value. With the implementation of immunotherapy, there is increasing interest in tumour immune response and the tumour microenvironment. In a search for better prognostic biomarkers for tumour recurrence and stage progression, in Paper II, we investigated the prognostic value of tumour infiltrating immune cells (CD4, CD8, CD25 and CD138) and previously investigated cell proliferation markers (Ki-67, PPH3 and MAI). Low Ki 67 and tumour multifocality were associated with increased recurrence risk. Recurrence risk was not affected by the composition of immune cells. For stage progression, the only prognostic immune cell marker was CD25. High values for MAI was also strongly associated with stage progression. However, in a multivariate analysis, the most prognostic feature was a combination of MAI and CD25. BCG-instillations in the bladder are indicated in intermediate and high-risk non-muscle invasive bladder cancer patients. This old-fashion immunotherapy has proved to reduce both recurrence- and progression-risk, although it is frequently followed by unpleasant side-effects. As many as 30-50% of high-risk patients receiving BCG instillations, fail by develop high-grade recurrences. They do not only suffer from unnecessary side-effects, but will also have a delay in further treatment. Together with colleagues at three different Dutch hospitals, in Paper III, we looked at the prognostic and predictive value of T1-substaging. A T1-tumour invades the lamina propria, and we wanted to separate those with micro- from those with extensive invasion. We found that BCG-failure was more common among patients with extensive invasion. Furthermore, T1-substaging was associated with both high-grade recurrence-free and progression-free survival. Finally, in Paper IV, we wanted to investigate the prognostic value of two classical immunohistochemical markers, p53 and CK20, and compare them with previously investigated proliferation markers. p53 is a surrogate marker for mutations in the gene TP53, considered to be a main characteristic for muscle-invasive tumours. CK20 is a surrogate marker for luminal tumours in the molecular classification of bladder cancer, and is frequently used to distinguish reactive urothelial changes from urothelial carcinoma in situ. We found both positivity for p53 and CK20 to be significantly associated with stage progression, although not performing better than WHO grade and stage. The proliferation marker MAI, had the highest prognostic value in our study. Any combination of variables did not perform better in a multivariate analysis than MAI alone

Gene expression analysis in breast cancer

Breast cancer is the most common type of cancer among females, both in incidence and death. As meaningful biological understanding of the disease is confounded by the existence of various molecular groups and sub-groups, the challenge for targeted drug development may lie in understanding the molecular mechanisms of various sub-groups in breast cancer. An in-house breast cancer gene expression dataset comprising 17 normal and 104 tumour samples was analysed to identify important genes and pathways relevant to various clinical parameters. Our results identified groups of patients with similar expression profiles, the possible biology driving them and the clinical implications. Comparing Normal and Cancer specimens’ gene expression profiles, TP53, along with cell cycle genes, were up-regulated in cancer samples. Embryonic stem cell pathway genes were up-regulated, while fatty acid biosynthesis pathways were down-regulated in tumors vs normal. The cancer specimens largely clustered with respect to ER status. Meta-analysis was performed on in-house datasets along with five public datasets to identify ER pathway genes. The analysis identified novel genes which had not been previously associated with ER-related pathways in cancer. Nuclear receptor pathways were up-regulated in ER-positive tumors/cell lines. Mining for ESR1-correlated genes across 5897 specimens identified FOXA1, SPDEF, C1ORF34 and GATA3 expression to be highly correlated. Three sub-clusters were identified among the ER-negative cluster. One represented ERBB2 over-expressing cluster. Additionally two unique groups of patients, with significant differences in survival, previously un-identified by other studies, were identified among the ER-negative cluster; a good prognosis cluster with high expression of Immune response genes; and a bad prognosis cluster with high expression of Ropporin, over-expression of which was also linked to high incidence of relapse in our study. siRNA knockdown of Ropporin (ROPN1 and ROPN1B) in the M14 melanoma cell line impaired cancer cell motility and invasion. Knockdown of ROPN1B in MDA-MB-435s reduced motility. In the first study of its kind our results validated the role of Ropporin in cancer cell motility and invasion. A list of 162 relapse-associated prognostically-important genes was used to develop a Neural Network back propagation model to predict the clinical outcomes. The model was successful in predicting relapse with 97.8% accuracy and outperformed existing models, indicating a strong possibility of its use as diagnostic model

Raman spectroscopy: techniques and applications in the life sciences

Raman spectroscopy is an increasingly popular technique in many areas including biology and medicine. It is based on Raman scattering, a phenomenon in which incident photons lose or gain energy via interactions with vibrating molecules in a sample. These energy shifts can be used to obtain information regarding molecular composition of the sample with very high accuracy. Applications of Raman spectroscopy in the life sciences have included quantification of biomolecules, hyperspectral molecular imaging of cells and tissue, medical diagnosis, and others. This review briefly presents the physical origin of Raman scattering explaining the key classical and quantum mechanical concepts. Variations of the Raman effect will also be considered, including resonance, coherent, and enhanced Raman scattering. We discuss the molecular origins of prominent bands often found in the Raman spectra of biological samples. Finally, we examine several variations of Raman spectroscopy techniques in practice, looking at their applications, strengths, and challenges. This review is intended to be a starting resource for scientists new to Raman spectroscopy, providing theoretical background and practical examples as the foundation for further study and exploration

Development of an image processing method for automated, non-invasive and scale-independent monitoring of adherent cell cultures

Adherent cell culture is a key experimental method for biological investigations in diverse areas such as developmental biology, drug discovery and biotechnology. Light microscopy-based methods, for example phase contrast microscopy (PCM), are routinely used for visual inspection of adherent cells cultured in transparent polymeric vessels. However, the outcome of such inspections is qualitative and highly subjective. Analytical methods that produce quantitative results can be used but often at the expense of culture integrity or viability. In this work, an imaging-based strategy to adherent cell cultures monitoring was investigated. Automated image processing and analysis of PCM images enabled quantitative measurements of key cell culture characteristics. Two types of segmentation algorithms for the detection of cellular objects on PCM images were evaluated. The first one, based on contrast filters and dynamic programming was quick (<1s per 1280×960 image) and performed well for different cell lines, over a wide range of imaging conditions. The second approach, termed ‘trainable segmentation’, was based on machine learning using a variety of image features such as local structures and symmetries. It accommodated complex segmentation tasks while maintaining low processing times (<5s per 1280×960 image). Based on the output from these segmentation algorithms, imaging-based monitoring of a large palette of cell responses was demonstrated, including proliferation, growth arrest, differentiation, and cell death. This approach is non-invasive and applicable to any transparent culture vessel, including microfabricated culture devices where a lack of suitable analytical methods often limits their applicability. This work was a significant contribution towards the establishment of robust, standardised, and affordable monitoring methods for adherent cell cultures. Finally, automated image processing was combined with computer-controlled cultures in small-scale devices. This provided a first demonstration of how adaptive culture protocols could be established; i.e. culture protocols which are based on cellular response instead of arbitrary time points

The biology of colorectal peritoneal malignancy and its prognostic significance.

Peritoneal metastasis, (CPM) develop in 15% of colorectal cancers. Cytoreductive surgery and heated intraperitoneal chemotherapy (CRS & HIPEC) aims to achieve macroscopic tumour resection and ablation of microscopic disease. However, 5-year survival varies widely and morbidity and mortality are relatively high. There is a need to improve patient selection and a paucity of research concerning CPM disease biology. Prognostic classifiers have been developed in a number of cancers, though none in CPM. I aimed to identify a multi-dimensional classifier to improve patient selection for CRS & HIPEC and to further the understanding of CPM disease biology. Firstly, clinical prognostic factors for patients with CPM undergoing CRS & HIPEC were identified by systematic review. Secondly, comprehensive molecular profiling of the transcriptome, epigenome and genome of CPM was performed to develop a prognostic multidimensional classifier. Finally, a potential biomarker of tumour recurrence following CRS & HIPEC was examined using circulating tumour DNA. This study has identified clinical and molecular features capable of predicting poor prognosis CPM and has potential applications in improved patient selection for treatment and the development of novel personalised treatments

Interpretation of Mutations, Expression, Copy Number in Somatic Breast Cancer: Implications for Metastasis and Chemotherapy

Breast cancer (BC) patient management has been transformed over the last two decades due to the development and application of genome-wide technologies. The vast amounts of data generated by these assays, however, create new challenges for accurate and comprehensive analysis and interpretation. This thesis describes novel methods for fluorescence in-situ hybridization (FISH), array comparative genomic hybridization (aCGH), and next generation DNA- and RNA-sequencing, to improve upon current approaches used for these technologies. An ab initio algorithm was implemented to identify genomic intervals of single copy and highly divergent repetitive sequences that were applied to FISH and aCGH probe design. FISH probes with higher resolution than commercially available reagents were developed and validated on metaphase chromosomes. An aCGH microarray was developed that had improved reproducibility compared to the standard Agilent 44K array, which was achieved by placing oligonucleotide probes distant from conserved repetitive sequences. Splicing mutations are currently underrepresented in genome-wide sequencing analyses, and there are limited methods to validate genome-wide mutation predictions. This thesis describes Veridical, a program developed to statistically validate aberrant splicing caused by a predicted mutation. Splicing mutation analysis was performed on a large subset of BC patients previously analyzed by the Cancer Genome Atlas. This analysis revealed an elevated number of splicing mutations in genes involved in NCAM pathways in basal-like and HER2-enriched lymph node positive tumours. Genome-wide technologies were leveraged further to develop chemosensitivity models that predict BC response to paclitaxel and gemcitabine. A type of machine learning, called support vector machines (SVM), was used to create predictive models from small sets of biologically-relevant genes to drug disposition or resistance. SVM models generated were able to predict sensitivity in two groups of independent patient data. High variability between individuals requires more accurate and higher resolution genomic data. However the data themselves are insufficient; also needed are more insightful analytical methods to fully exploit these data. This dissertation presents both improvements in data quality and accuracy as well as analytical procedures, with the aim of detecting and interpreting critical genomic abnormalities that are hallmarks of BC subtypes, metastasis and therapy response

Application of data science to inform surface engineering for in vitro neural stem cell control

The interest in the clinical use of stem cell therapies is increasing rapidly, with a need for more control over cell populations cultured/expanded in vitro. This is particularly relevant for the treatment of neurological disorders such as Parkinson's disease where positive outcome measures of clinical trials will be limited by the number of derived neurons and their specific sub-types. The aim is to generate enhanced neural cell populations from stem cells through the design of the cell-material interface. The niche micro-environment is complex, being responsible for cell attachment, proliferation and differentiation. Material engineering approaches to better control cell responses have looked towards surface chemical, topographical and mechanical cues. The many permutations of these factors pose a major challenge in the optimisation of biomaterial design. Machine learning techniques will be used to assess the impact of surface properties on the biological micro-environment. Cell interaction/response provides computational outputs, with input variables being derived from material properties such as surface chemical characteristics (logP, charge, density, wettability, etc.) and topography (nano- and micro-scale, aspect ratio, etc). The aim is to unravel the relationship between cells and biomaterial surface of in vitro cell culture. In vitro experiments and in silico modelling will continually inform each other towards the optimisation of neural cell characteristic responses

Application of data science to inform surface engineering for in vitro neural stem cell control

The interest in the clinical use of stem cell therapies is increasing rapidly, with a need for more control over cell populations cultured/expanded in vitro. This is particularly relevant for the treatment of neurological disorders such as Parkinson's disease where positive outcome measures of clinical trials will be limited by the number of derived neurons and their specific sub-types. The aim is to generate enhanced neural cell populations from stem cells through the design of the cell-material interface. The niche micro-environment is complex, being responsible for cell attachment, proliferation and differentiation. Material engineering approaches to better control cell responses have looked towards surface chemical, topographical and mechanical cues. The many permutations of these factors pose a major challenge in the optimisation of biomaterial design. Machine learning techniques will be used to assess the impact of surface properties on the biological micro-environment. Cell interaction/response provides computational outputs, with input variables being derived from material properties such as surface chemical characteristics (logP, charge, density, wettability, etc.) and topography (nano- and micro-scale, aspect ratio, etc). The aim is to unravel the relationship between cells and biomaterial surface of in vitro cell culture. In vitro experiments and in silico modelling will continually inform each other towards the optimisation of neural cell characteristic responses