The MoNuSAC 2020 challenge was hosted at the ISBI conference, where winners were announced. Challenge organizers, in addition to leaderboard, released evaluation code and visualisations of prediction masks “top 5” teams. This shows a very high level transparency, provides unique opportunity better understand results. Our analysis all data, however, three different problems computation metric us...

A variety of computational intelligence approaches to nuclei segmentation in the microscope images of fine needle biopsy material is presented in this chapter. The segmentation is one of the most important steps of the automatic medical diagnosis based on the analysis of the microscopic images, and is crucial to making a correct diagnostic decision. Due to complex nature of biological images, s...

We describe here a framework for robust and automated extraction of nuclei from cluttered 3D images, enabling the quantification of parameters including number of cells, number of mitoses and mitotic orientation. The proposed framework combines novel PDE-based image processing techniques for image filtering and analysis. Starting from a 2-channel staining of nuclei and membrane markers of the t...

The objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. This problem is challenging due to the large variations of these components features (size, shape, orientation or texture). In this paper we present an automatic technique to robustly identify the epithelial nuclei (crypt) against interstitial nuclei in microscopic images taken...

In this paper, we propose a two-phase approach to nuclei segmentation/classification in Pap smear test images. The first phase, the segmentation phase, includes a morphological algorithm (watershed) and a hierarchical merging algorithm (waterfall). In the merging step, waterfall uses spectral and shape information as well as the class information. In the second phase, classification, the goal i...

Segmentation and parcellation of the thalamus is an important step in providing volumetric assessment of the impact of disease n brain structures. Conventionally, segmentation is carried out on T1-weighted magnetic resonance (MR) images and nuclear parcellation using diffusion weighted MR images. We present the first fully automatic method that incorporates both tissue contrasts and several der...

Methods based on combinatorial graph cut algorithms received a lot of attention in the recent years for their robustness as well as reasonable computational demands. These methods are built upon an underlying Maximum a Posteriori estimation of Markov Random Fields and are suitable to solve accurately many different problems in image analysis, including image segmentation. In this paper we prese...

Automated histological grading of tissue biopsies for clinical cancer care is a challenging problem that requires sophisticated algorithms for image segmentation, tissue architecture characterization, global texture feature extraction, and high-dimensional clustering and classification algorithms. Currently there are no automatic image-based grading systems for establishing the pathology of can...

Hepatocellular carcinoma (HCC) is graded mainly based on the characteristics of liver cell nuclei. This paper proposes a textural feature descriptor and a novel computational method for classifying liver cell nuclei and grading the HCC histological images. The proposed textural feature descriptor observes local and spatial characteristics of the texture patterns by using multifractal computatio...