Knowledge-Guided Semantic Indexing of Breast Cancer Histopathology Images

Abstract

Narrowing the semantic gap represents one of the most outstanding challenges in medical image analysis and indexing. This paper introduces a medical knowledge-guided paradigm for semantic indexing of histopathology images, applied to breast cancer grading (BCG). Our method improves pathologists' current manual procedures consistency by employing a semantic indexing technique, according to a rule-based decision system related to Nottingham BCG system. The challenge is to move from the medical concepts/ rules related to the BCG, to the computer vision (CV) concepts and symbolic rules, to design a future generic framework-following Web Ontology Language standards-for an semi-automatic generation of CV rules. The effectiveness of this approach was experimentally validated over six breast cancer cases consisting of 7000 frames with domain knowledge from experts of Singapore National University Hospital, Pathology Department. Our method provides pathologists a robust and consistent tool for BCG and opens interesting perspectives for the semantic retrieval and visual positioning.

Charisma: An integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting

“…Tutac et al (2008) translates image features into a set of symbolic rules. These rules allow an algorithm to first detect single objects, i.e., nuclei and cells, and then evaluate their relationship.…”

Time-efficient sparse analysis of histopathological whole slide images

“…• Learning bases using projected gradient descent: hold the S in (8), and evaluate the B. Thus, (8) becomes the following minimization problem:…”

“…Doyle et al [6,7] proposed a method based on geometrical features, to distinguish between healthy tissue, low grade and high grade cancer. Tutac et al [8] initiated an innovative knowledge guided approach relying on the prior modeling of medical knowledge using ontology designed according to the clinical standard called Nottingham Grading System [9]. An extension to this work involving multi-scale approaches was proposed by Dalle et al [10].…”

Automatic breast cancer grading of histopathological images

“…Variability among pathologists have been observed in clinical practice [1]. In a large hospital, a pathologist typically handles 100 grading cases per day, each consisting of about 2000 image frames [2]. It is, therefore, a very tedious and time-consuming task.…”

“…Existing methods, except [2] and [3], handle only one of these criteria for histological images. This paper presents a multi-resolution method for breast cancer grading that combines the three criteria.…”