Literature DB >> 30219733

A smart and operator independent system to delineate tumours in Positron Emission Tomography scans.

Albert Comelli1, Alessandro Stefano2, Giorgio Russo3, Maria Gabriella Sabini4, Massimo Ippolito5, Samuel Bignardi6, Giovanni Petrucci7, Anthony Yezzi6.   

Abstract

Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automatically stops when a "cancer-free" slice is identified. User intervention is limited at drawing an initial rough contour around the cancer region. By design, the algorithm performs the segmentation minimizing any dependence from the initial input, so that the final result is extremely repeatable. To assess the performances under different conditions, our system is evaluated on a dataset comprising five synthetic experiments and fifty oncological lesions located in different anatomical regions (i.e. lung, head and neck, and brain) using PET studies with 18F-fluoro-2-deoxy-d-glucose and 11C-labeled Methionine radio-tracers. Results on synthetic lesions demonstrate enhanced performances when compared against the most common PET segmentation methods. In clinical cases, the proposed system produces accurate segmentations (average dice similarity coefficient: 85.36 ± 2.94%, 85.98 ± 3.40%, 88.02 ± 2.75% in the lung, head and neck, and brain district, respectively) with high agreement with the gold standard (determination coefficient R2 = 0.98). We believe that the proposed system could be efficiently used in the everyday clinical routine as a medical decision tool, and to provide the clinicians with additional information, derived from PET, which can be of use in radiation therapy, treatment, and planning.
Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  18F-fluoro-2-deoxy-d-glucose and 11C-labeled methionine PET imaging; Active contour algorithm; Biological target volume; Cancer segmentation

Mesh:

Substances:

Year:  2018        PMID: 30219733     DOI: 10.1016/j.compbiomed.2018.09.002

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  6 in total

Review 1.  Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities.

Authors:  Navid Hasani; Faraz Farhadi; Michael A Morris; Moozhan Nikpanah; Arman Rhamim; Yanji Xu; Anne Pariser; Michael T Collins; Ronald M Summers; Elizabeth Jones; Eliot Siegel; Babak Saboury
Journal:  PET Clin       Date:  2022-01

2.  Development of a new fully three-dimensional methodology for tumours delineation in functional images.

Authors:  Albert Comelli; Samuel Bignardi; Alessandro Stefano; Giorgio Russo; Maria Gabriella Sabini; Massimo Ippolito; Anthony Yezzi
Journal:  Comput Biol Med       Date:  2020-03-16       Impact factor: 4.589

3.  Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement.

Authors:  Rulon Mayer; Charles B Simone; Baris Turkbey; Peter Choyke
Journal:  Quant Imaging Med Surg       Date:  2021-01

Review 4.  Artificial intelligence for molecular neuroimaging.

Authors:  Amanda J Boyle; Vincent C Gaudet; Sandra E Black; Neil Vasdev; Pedro Rosa-Neto; Katherine A Zukotynski
Journal:  Ann Transl Med       Date:  2021-05

5.  Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT.

Authors:  Alessandro Stefano; Mauro Gioè; Giorgio Russo; Stefano Palmucci; Sebastiano Emanuele Torrisi; Samuel Bignardi; Antonio Basile; Albert Comelli; Viviana Benfante; Gianluca Sambataro; Daniele Falsaperla; Alfredo Gaetano Torcitto; Massimo Attanasio; Anthony Yezzi; Carlo Vancheri
Journal:  Diagnostics (Basel)       Date:  2020-05-15

6.  A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method.

Authors:  Alessandro Stefano; Albert Comelli; Valentina Bravatà; Stefano Barone; Igor Daskalovski; Gaetano Savoca; Maria Gabriella Sabini; Massimo Ippolito; Giorgio Russo
Journal:  BMC Bioinformatics       Date:  2020-09-16       Impact factor: 3.169

  6 in total

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