Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure

Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression

The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable

Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation

The utilisation of deep learning segmentation algorithms that learn complex organs and tissue patterns and extract essential regions of interest from the noisy background to improve the visual ability for medical image diagnosis has achieved impressive results in Medical Image Computing (MIC). This thesis focuses on retinal blood vessel segmentation tasks, providing an extensive literature review of deep learning-based medical image segmentation approaches while comparing the methodologies and empirical performances. The work also examines the limitations of current state-of-the-art methods by pointing out the two significant existing limitations: data size constraints and the dependency on high computational resources. To address such problems, this work proposes a novel efficient, simple multiview learning framework that contrastively learns invariant vessel feature representation by comparing with multiple augmented views by various transformations to overcome data shortage and improve generalisation ability. Moreover, the hybrid network architecture integrates the attention mechanism into a Convolutional Neural Network to further capture complex continuous curvilinear vessel structures. The result demonstrates the proposed method validated on the CHASE-DB1 dataset, attaining the highest F1 score of 83.46% and the highest Intersection over Union (IOU) score of 71.62% with UNet structure, surpassing existing benchmark UNet-based methods by 1.95% and 2.8%, respectively. The combination of the metrics indicates the model detects the vessel object accurately with a highly coincidental location with the ground truth. Moreover, the proposed approach could be trained within 30 minutes by consuming less than 3 GB GPU RAM, and such characteristics support the efficient implementation for real-world applications and deployments

Características morfológicas e aspectos funcionais do telencéfalo de Betta splendens regan 1910

Betta splendens é um peixe actinopterígeo da subordem dos anabatídeos que apresenta marcado comportamento agonista resultante do territorialismo que o caracteriza. Esse comportamento, ao lado de uma corte e cuidado com a prole elaborados, sugere funções telencefálicas altamente desenvolvidas para este peixe. Em razão disso, e de apresentar uma série de características favoráveis quanto a sua reprodução e manutenção, ele constitui-se em um modelo potencial para as Neurociências, principalmente no que tange o estudo da agressividade. Com o objetivo de caracterizar a estrutura e possíveis funções do telencéfalo, B. splendens machos e fêmeas foram utilizados para (1) elaborar um mapa neuroanatômico com base na topologia, topografia e citoarquitetura dos grupos celulares presentes nessa região do encéfalo, empregando as técnicas histológicas da Hematoxilina- Eosina (HE) e de Nissl, e determinar a densidade númérica para neurônios e células gliais nos núcleos supracomissural (Vs) e pós-comissural (Vp) do telencéfalo; e, (2) identificar a atividade da enzima NADPH diaforase (NADPH-d) por método histoquímico nas diferentes áreas e grupos celulares telencefálicos deste peixe frente a um paradigma comportamental para a agressividade. A primeira abordagem revelou que o telencéfalo de machos e fêmeas de B. splendens apresenta uma estrutura similar, com um bulbo olfatório composto por cinco camadas celulares concêntricas e hemisférios telencefálicos constituídos por 16 e 8 grupos celulares distintos em suas regiões dorsal e ventral, respectivamente, e que não há dimorfismo sexual quanto à densidade numérica para neurônios e células gliais de Vs e Vp, áreas homólogas à amígdala medial de mamíferos. O segundo trabalho demonstou que a marcação para a atividade da enzima NADPH-d no telencéfalo de B. splendens é específica para cada sexo, com mais corpos celulares marcados no telencéfalo ventral do que no dorsal. Mostrou também que o paradigma comportamental voltado a promover a agressividade gerou um aumento da intensidade da marcação como do número de estruturas marcadas no telencéfalo deste peixe. Tanto a caracterização morfológica quanto funcional do telencéfalo de machos e fêmeas de B. splendens aqui realizadas fornecem novos dados que vem a contribuir para a adoção dessa espécie como um modelo não mamífero para o estudo da neurobiologia da agressividade.Betta splendens is an Actinopterygii fish of the suborder of the Anabantoidei that shows pronounced agonistic behavior resulting from its territorialism. This kind of behavior beside to an intrincate courtship and complex parental care suggest highly developed telencephalic functions to this species. Due to aforementioned and to other characteristics as an easy reproduction and simple maintenance, this species constitutes a potential model for the neuroscience area, mainly related to the study of the aggressiveness. In order to characterize the telencephalic structures relating them to their putative roles male and female B. splendes were used to: (1) construct a neuroanatomical map based on the topology, topography and cytoarchitecture of the cellular groups present in this brain region using the histological techniques of the Hematoxylin – Eosin (HE) and Nissl, as well as, define the numerical density for neurons and glial cells from supracomissural (Vs) and postcomissural (Vp) nuclei of the telencephalon. (2) Identify the activity of the NADPH diaphorase enzyme (NADPH-d) by its histochemical method to the different areas and structures of the telencephalon submitted to a behavioral paradigm for the aggressiveness. The first approach revealed that the telencephalon of males and females of the B. splendens has the same structure, with an olfactory bulb composed of five concentric cellular layers and telencephalic hemispheres constituted by 16 and 8 distinct cell groups in their dorsal and ventral regions respectively, and that there is no sexual dimorphism to the numerical density for neurons and glial cells of the Vs and Vp, homologous structures to the medial amygdala of mammals. The second work demonstrated that the activity of the enzyme NADPH-d in the telencephalon of B. splendens has a specific pattern for each sex, with more cellular bodies marked in ventral telencephalon than in dorsal one. It also showed that the behavioral paradigm for aggressiveness promoted an increase in the intensity as well as in the number of structures marked in the telencephalon of this fish. Both the morphological and functional characterization of the telencephalon of males and females of the B. splendens carried out here provide new data which contributes to the adoption of this species as a non-mammalian model for the study of the aggressiveness in the neuroscience fields

A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology

In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resolution histology images are subject to high variance in appearance, sourcing either from the acquisition devices or the H\&E staining process. Nuclei segmentation is an important task, as it detects the nuclei cells over background tissue and gives rise to the topology, size, and count of nuclei which are determinant factors for cancer detection. Yet, it is a fairly time consuming task for pathologists, with reportedly high subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern Artificial Intelligence (AI) models enable the automation of nuclei segmentation. This can reduce the subjectivity in analysis and reading time. This paper provides an extensive review, beginning from earlier works use traditional image processing techniques and reaching up to modern approaches following the Deep Learning (DL) paradigm. Our review also focuses on the weak supervision aspect of the problem, motivated by the fact that annotated data is scarce. At the end, the advantages of different models and types of supervision are thoroughly discussed. Furthermore, we try to extrapolate and envision how future research lines will potentially be, so as to minimize the need for labeled data while maintaining high performance. Future methods should emphasize efficient and explainable models with a transparent underlying process so that physicians can trust their output.Comment: 47 pages, 27 figures, 9 table

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