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Morphology of cell nuclei is a central aspect in many histopathological studies, in particular in breast cancer grading. Therefore, the automatic detection and extraction of cell nuclei from microscopic images obtained from cancer tissue slides is one of the most important problems in digital histopathology. We propose to tackle the problem using a model based on marked point processes (MPP), a methodology for extraction of multiple objects from images. The advantage of MPP based models is their ability to take into account the geometry of objects; and the information about their spatial repartition in the image. Previously, the MPP models have been applied for the extraction of objects of simple geometrical shapes. For histological grading, a morphological criterion known as nuclear pleomorphism corresponding to fine morphological differences between the nuclei is assessed by pathologists. Therefore, the accurate delineation of nuclei became an issue of even greater importance than optimal nuclei detection. Recently, the MPP framework has been defined on the space of arbitrarily-shaped objects allowing more accurate extraction of complex-shaped objects. The nuclei often appear joint or even overlap in histopathological images. The model still allows to extract them as individual joint or overlapping objects without discarding the overlapping parts and therefore without significant loss in delineation precision. We aim to compare the MPP model with two state-of-the-art methods selected from a comprehensive review of the available methods. The experiments are performed using a database of H&E stained breast cancer images covering a wide range of histological grades.
Kulikova et al. (Tue,) studied this question.