Magnetic Resonance Imaging Virtual Histopathology from Weakly Paired Data

Amaury Leroy, Kumar Shreshtha, Marvin Lerousseau, Théophraste Henry, Théo ESTIENNE, Marion Classe, Nikos Paragios, Vincent Grégoire, Eric Deutsch

Keywords: Synthetic Histopathology, Generative Model, Whole Slide Image, Magnetic Resonance Imaging, Unsupervised Learning

TL;DR: We propose an end-to-end generative pipeline to infer histological whole slide image from MR imaging, bridging the gap between radiology and biology.

Abstract: The pathological analysis of biopsy specimens is essential to cancer diagnosis, treatment selection and prognosis. However, biopsies are only taken from part of the tumor and cannot assess the full cellular extension. Such information is essential to delineate as accurately as possible the tumor volume on a three-dimensional basis. Furthermore, they require highly qualified personnel and are associated with significant risks. The aim of our work is to provide alternative means to gather clinical information related to histology through MR image translation towards virtual pathological content generation. Conventional approaches to address this objective exploit paired data that is cumbersome to achieve due to tissue collapse and deformation, different resolution scales and absence of plane correspondences. In this paper, we introduce a versatile, scalable and robust closed-loop dual synthesis concept that composes two generation mechanisms - cycle-consistent generative adversarial networks -, one exploring weakly paired data and a subsequent harnessing virtually generated paired correspondences. The clinical relevance and interest of our framework are demonstrated in prostate cancer patients. Qualitative clinical assessment and quantitative reconstruction measurements demonstrate the potential of our approach.