Assessment of algorithms for mitosis detection in breast cancer histopathology images

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists

Shape and Texture Recognition for Automated Analysis of Pathology Images.

This research project is concerned with automated analysis of microscopic images used in clinical pathology for diagnosing disease. Application of computer vision methods can improve the accuracy, reliability and availability of tests, reduce the associated costs and ultimately improve patient outcomes. Three different areas of pathology are covered: • identification of clustered nuclei and detection of chromosomal abnormalities in DAPI-stained samples,• diagnosis of auto-immune diseases from indirect immunofluorescence (IIP) images, and • detection of dividing nuclei in H&E stained histopathology sections. Despite the diversity of these application domains, the techniques used for their analysis are similar. For cluster identification in DARI images we focus on object shape and extend existing methods of shape analysis with novel measurements of the boundary profile which detect notches between overlapping nuclei in a cluster. For abnormality detection we focus on texture and develop a novel decision-tree dictionary for patch quantisation. We continue to focus on texture for IIP images, developing suitable isotropic measurements as well as exploring the connections between classification of individual cells and whole patient samples. Detection of dividing cells in tissue sections requires a combined assessment of shape, texture and colour in order to fully represent all relevant facets of the object. Here we develop a method for stain normalisation which efficiently compensates for batch variations in stain strength and proportions, followed by a full pipe-line of segmentation, feature extraction and classification, resolving issues of class imbalance implicit in detection of rare objects. We develop an efficient and effective segmentation method, which is free of weight parameters and adaptable for use in different imaging modalities. We explore a variety of classifier types and ensemble structures, and suggest promising directions of future development in the broad application area of pathology image analysis

Shape and Texture Recognition for Automated Analysis of Pathology Images

This research project is concerned with automated analysis of microscopic images used in clinical pathology for diagnosing disease. Application of computer vision methods can improve the accuracy, reliability and availability of tests, reduce the associated costs and ultimately improve patient outcomes. Three different areas of pathology are covered: 1. identification of clustered nuclei and detection of chromosomal abnormalities in DAPI-stained samples, 2. diagnosis of auto-immune diseases from indirect immuno fluorescence (IIF) images, and 3. detection of dividing nuclei in H&E stained histopathology sections. Despite the diversity of these application domains, the techniques used for their analysis are similar. For cluster identification in DAPI images we focus on object shape and extend existing methods of shape analysis with novel measurements of the boundary profile which detect notches between overlapping nuclei in a cluster. For abnormality detection we focus on texture and develop a novel decision-tree dictionary for patch quantisation. We continue to focus on texture for IIF images, developing suitable isotropic measurements as well as exploring the connections between classification of individual cells and whole patient samples. Detection of dividing cells in tissue sections requires a combined assessment of shape, texture and colour in order to fully represent all relevant facets of the object. Here we develop a method for stain normalisation which efficiently compensates for batch variations in stain strength and proportions, followed by a full pipe-line of segmentation, feature extraction and classification, resolving issues of class imbalance implicit in detection of rare objects. We develop an efficient and effective segmentation method, which is free of weight parameters and adaptable for use in different imaging modalities. We explore a variety of classifer types and ensemble structures, and suggest promising directions of future development in the broad application area of pathology image analysis

Shape and Texture Recognition for Automated Analysis of Pathology Images.

This research project is concerned with automated analysis of microscopic images used in clinical pathology for diagnosing disease. Application of computer vision methods can improve the accuracy, reliability and availability of tests, reduce the associated costs and ultimately improve patient outcomes. Three different areas of pathology are covered: • identification of clustered nuclei and detection of chromosomal abnormalities in DAPI-stained samples,• diagnosis of auto-immune diseases from indirect immunofluorescence (IIP) images, and • detection of dividing nuclei in H&E stained histopathology sections. Despite the diversity of these application domains, the techniques used for their analysis are similar. For cluster identification in DARI images we focus on object shape and extend existing methods of shape analysis with novel measurements of the boundary profile which detect notches between overlapping nuclei in a cluster. For abnormality detection we focus on texture and develop a novel decision-tree dictionary for patch quantisation. We continue to focus on texture for IIP images, developing suitable isotropic measurements as well as exploring the connections between classification of individual cells and whole patient samples. Detection of dividing cells in tissue sections requires a combined assessment of shape, texture and colour in order to fully represent all relevant facets of the object. Here we develop a method for stain normalisation which efficiently compensates for batch variations in stain strength and proportions, followed by a full pipe-line of segmentation, feature extraction and classification, resolving issues of class imbalance implicit in detection of rare objects. We develop an efficient and effective segmentation method, which is free of weight parameters and adaptable for use in different imaging modalities. We explore a variety of classifier types and ensemble structures, and suggest promising directions of future development in the broad application area of pathology image analysis

Shape and Texture Recognition for Automated Analysis of Pathology Images

This research project is concerned with automated analysis of microscopic images used in clinical pathology for diagnosing disease. Application of computer vision methods can improve the accuracy, reliability and availability of tests, reduce the associated costs and ultimately improve patient outcomes. Three different areas of pathology are covered: 1. identification of clustered nuclei and detection of chromosomal abnormalities in DAPI-stained samples, 2. diagnosis of auto-immune diseases from indirect immuno fluorescence (IIF) images, and 3. detection of dividing nuclei in H&E stained histopathology sections. Despite the diversity of these application domains, the techniques used for their analysis are similar. For cluster identification in DAPI images we focus on object shape and extend existing methods of shape analysis with novel measurements of the boundary profile which detect notches between overlapping nuclei in a cluster. For abnormality detection we focus on texture and develop a novel decision-tree dictionary for patch quantisation. We continue to focus on texture for IIF images, developing suitable isotropic measurements as well as exploring the connections between classification of individual cells and whole patient samples. Detection of dividing cells in tissue sections requires a combined assessment of shape, texture and colour in order to fully represent all relevant facets of the object. Here we develop a method for stain normalisation which efficiently compensates for batch variations in stain strength and proportions, followed by a full pipe-line of segmentation, feature extraction and classification, resolving issues of class imbalance implicit in detection of rare objects. We develop an efficient and effective segmentation method, which is free of weight parameters and adaptable for use in different imaging modalities. We explore a variety of classifer types and ensemble structures, and suggest promising directions of future development in the broad application area of pathology image analysis

Poor relief and welfare : a comparative study of the Belper and Cheltenham Poor Law Unions, 1780 to 1914

There are few local studies of a comparative nature encompassing poor law unions in different regions. This thesis is unique in considering a union in the north midlands and one bordering the south-west, from 1780 to 1914. The provision of relief in Cheltenham and Belper is set in the context of social and economic conditions in these two areas. Were Cheltenham and Belper different in their management of their poor between 1770 and 1914, and how did poor relief in these two unions conform or differ to the specifications laid down in the 1834 Act? Chapter 1 looks at relief under the old poor law, while chapter 2 considers the manner in which the unions were formed. Chapters 3, 4 and 6 analyse the workhouse and union populations at various times, and chapter 5 investigates charity and its assistance to the poor. Several major themes are looked at including emigration, vagrants, the children and aged. Cheltenham and Belper managed their poor in a similar manner, except most notably with regard to assisted emigration. Only Cheltenham used this to reduce pauperism. It provided out-relief for a greater number of paupers than Belper, and its expenditure per head was much higher. Workhouse populations were very distinctive in 1851. Belper had a high percentage of children and female able-bodied paupers at that time. By 1911 the workhouse populations had become more similar in both unions, being dominated by the elderly, sick and infirm. The thesis argues for general trends, observes a common trajectory of change, assesses charity alongside formal relief, and shows how interestingly different socio-economic contexts affected the comparative details and nature of pauperism. It thus invites further comparative research into the varied regional application of the 1834 Poor Law Amendment Act, using the benchmarks and salient features highlighted here.EThOS - Electronic Theses Online ServiceGBUnited Kingdo