Semi-automatic lymphoma detection and segmentation using fully conditional random fields. (December 2018)

• Record Type: Journal Article
• Authors: Yu, Yuntao; Decazes, Pierre; Lapuyade-Lahorgue, Jérôme; Gardin, Isabelle; Vera, Pierre; Ruan, Su
• Is Part Of: Computerized medical imaging and graphics. Volume 70(2018)
• Page Start: 1
• Page End: 7
• Publication Date: 2018-12
• Subjects: Positron Emission Tomography (PET) -- Lymphoma detection and segmentation -- Fully connected conditional random fields -- Anatomical atlas; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2018.09.001
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Fully Conditional Random Fields (CRF) are studied for the detection and segmentation of lymphoma from PET/CT. Lymphomas can be everywhere in the body. CRF can model a large dependency in the hidden states. The energy terms used in CRFs are estimated in an unsupervised way. The results obtained over 11 patients are very encouraging. Abstract: The detection and delineation of the lymphoma volume are a critical step for its treatment and its outcome prediction. Positron Emission Tomography (PET) is widely used for lymphoma detection. Two common types of approaches can be distinguished for lymphoma detection and segmentation in PET. The first one is ROI dependent which needs a ROI defined by physicians. The second one is based on machine learning methods which need a large learning database. However, such a large standard database is quite rare in medical field. Considering these problems, we propose a new approach that combines PET (metabolic information) with CT (anatomical information). Our approach is semi-automatic, it consists of three steps. First, an anatomical multi-atlas segmentation is applied on CT to locate and remove the organs having physiologic hypermetabolism in PET. Then, CRFs (Conditional Random Fields) detect and segment a set of possible lymphoma volumes in PET. The conditional probabilities used in CRFs are usually estimated by a learning step. In this work, we propose to estimate them in an unsupervised way. The final step is to visualize the detected lymphoma volumes and select the real ones by simply clicking on them. The false detection is low thanks to the first step. Our method is tested on 11 patients. The rate of good detection of lymphoma is 100%. The average of Dice indexes for measuring the lymphoma segmentation performance is 84.4% compared to the manual lymphoma segmentation. Comparing with other methods in terms of Dice index shows the best performance of our method.