Tumor proliferation assessment of whole slide images

Rousson, Mikael (ContextVision AB, Linkoping, Sweden) ; Hedlunda, Martin (ContextVision AB, Linkoping, Sweden) ; Anderssona, Mats (ContextVision AB, Linkoping, Sweden) ; Jacobsson, Ludwig (ContextVision AB, Linkoping, Sweden) ; Lathen, Gunnar (ContextVision AB, Linkoping, Sweden) ; Norella, Bjorn (ContextVision AB, Linkoping, Sweden) ; Jimenez-del-Toro, Oscar (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Atzori, Manfredo (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

Grading whole slide images (WSIs) from patient tissue samples is an important task in digital pathology, particularly for diagnosis and treatment planning. However, this visual inspection task, performed by pathologists, is inherently subjective and has limited reproducibility. Moreover, grading of WSIs is time consuming and expensive. Designing a robust and automatic solution for quantitative decision support can improve the objectivity and reproducibility of this task. This paper presents a fully automatic pipeline for tumor proliferation assessment based on mitosis counting. The approach consists of three steps: i) region of interest selection based on tumor color characteristics, ii) mitosis counting using a deep network based detector, and iii) grade prediction from ROI mitosis counts. The full strategy was submitted and evaluated during the Tumor Proliferation Assessment Challenge (TUPAC) 2016. TUPAC is the rst digital pathology challenge grading whole slide images, thus mimicking more closely a real case scenario. The pipeline is extremely fast and obtained the 2nd place for the tumor proliferation assessment task and the 3rd place in the mitosis counting task, among 17 participants. The performance of this fully automatic method is similar to the performance of pathologists and this shows the high quality of automatic solutions for decision support.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Date:
2018-03
Pagination:
9 p.
Published in:
Medical Imaging 2018 (SPIE) : Digital Pathology
Numeration (vol. no.):
March 2018, vol. 10581
DOI:
ISBN:
9781510616516
Appears in Collection:



 Record created 2018-10-22, last modified 2018-12-20

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