Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks

We investigate glandular structure segmentation in colon histology images as a window-based classification problem. We compare and combine methods based on fine-tuned convolutional neural networks (CNN) and hand-crafted features with support vector machines (HC-SVM). On 85 images of H&E-stained tissue, we find that fine-tuned CNN outperforms HC-SVM in gland segmentation measured by pixel-wise Jaccard and Dice indices. For HC-SVM we further observe that training a second-level window classifier on the posterior probabilities - as an output refinement - can substantially improve the segmentation performance. The final performance of HC-SVM with refinement is comparable to that of CNN. Furthermore, we show that by combining and refining the posterior probability outputs of CNN and HC-SVM together, a further performance boost is obtained

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

Structure Prediction for Gland Segmentation with Hand-Crafted and Deep Convolutional Features

We present a novel method to segment instances of glandular structures from colon histopathology images. We use a structure learning approach which represents local spatial configurations of class labels, capturing structural information normally ignored by sliding-window methods. This allows us to reveal different spatial structures of pixel labels (e.g., locations between adjacent glands, or far from glands), and to identify correctly neighboring glandular structures as separate instances. Exemplars of label structures are obtained via clustering and used to train support vector machine classifiers. The label structures predicted are then combined and post-processed to obtain segmentation maps. We combine hand-crafted, multi-scale image features with features computed by a deep convolutional network trained to map images to segmentation maps. We evaluate the proposed method on the public domain GlaS data set, which allows extensive comparisons with recent, alternative methods. Using the GlaS contest protocol, our method achieves the overall best performance

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Gland segmentation in gastric histology images: detection of intestinal metaplasia

Gastric cancer is one of the most frequent causes of cancer-related deaths worldwide. Gastric intestinal metaplasia (IM) of the mucosa of the stomach has been found to increase the risk of gastric cancer and is considered as one of the precancerous lesions. Therefore, early detection of IM may have a valuable role in histopathological risk assessment regarding the possibility of progression to cancer. Accurate segmentation and analysis of gastric glands from the histological images plays an important role in the diagnostic confirmation of IM. Thus, in this paper, we propose a framework for segmentation of gastric glands and detection of IM. More specifically, we propose the GAGL-Net for the segmentation of glands. Then, based on two features of the extracted glands we classify the tissues into normal and IM cases. The results showed that the proposed gland segmentation approach achieves an F1 score equal to 0.914. Furthermore, the proposed methodology shows great potential for the IM detection achieving an accuracy score equal to 96.6%. To evaluate the efficiency of the proposed methodology we used a publicly available dataset and we created the GAGL dataset consisting of 59 Whole Slide Images (WSI) including both IM and normal cases

Gland Instance Segmentation in Colon Histology Images

This thesis looks at approaches to gland instance segmentation in histology images. The aim is to find suitable local image representations to describe the gland structures in images with benign tissue and those with malignant tissue and subsequently use them for design of accurate, scalable and flexible gland instance segmentation methods. The gland instance segmentation is a clinically important and technically challenging problem as the morphological structure and visual appearance of gland tissue is highly variable and complex. Glands are one of the most common organs in the human body. The glandular features are present in many cancer types and histopathologists use these features to predict tumour grade. Accurate tumour grading is critical for prescribing suitable cancer treatment resulting in improved outcome and survival rate. Different cancer grades are reflected by differences in glands morphology and structure. It is therefore important to accurately segment glands in histology images in order to get a valid prediction of tumour grade. Several segmentation methods, including segmentation with and without pre-classification, have been proposed and investigated as part of the research reported in this thesis. A number of feature spaces, including hand-crafted and deep features, have been investigated and experimentally validated to find a suitable set of image attributes for representation of benign and malignant gland tissue for the segmentation task. Furthermore, an exhaustive experimental examination of different combinations of features and classification methods have been carried out using both qualitative and quantitative assessments, including detection, shape and area fidelity metrics. It has been shown that the proposed hybrid method combining image level classification, to identify images with benign and malignant tissue, and pixel level classification, to perform gland segmentation, achieved the best results. It has been further shown that modelling benign glands using a three-class model, i.e. inside, outside and gland boundary, and malignant tissue using a two-class model is the best combination for achieving accurate and robust gland instance segmentation results. The deep learning features have been shown to overall outperform handcrafted features, however proposed ring-histogram features still performed adequately, particularly for segmentation of benign glands. The adopted transfer-learning model with proposed image augmentation has proven very successful with 100% image classification accuracy on the available test dataset. It has been shown that the modified object- level Boundary Jaccard metric is more suitable for measuring shape similarity than the previously used object-level Hausdorff distance, as it is not sensitive to outliers and could be easily integrated with region- based metrics such as the object-level Dice index, as contrary to the Hausdorff distance it is bounded between 0 and 1. Dissimilar to most of the other reported research, this study provides comprehensive comparative results for gland segmentation, with a large collection of diverse types of image features, including hand-crafted and deep features. The novel contributions include hybrid segmentation model superimposing image and pixel level classification, data augmentation for re-training deep learning models for the proposed image level classification, and the object- level Boundary Jaccard metric adopted for evaluation of instance segmentation methods