Loading...

Esophageal gross tumor volume segmentation using a 3D convolutional neural network

Yousefi, S ; Sharif University of Technology | 2018

591 Viewed
  1. Type of Document: Article
  2. DOI: 10.1007/978-3-030-00937-3_40
  3. Publisher: Springer Verlag , 2018
  4. Abstract:
  5. Accurate gross tumor volume (GTV) segmentation in esophagus CT images is a critical task in computer aided diagnosis (CAD) systems. However, because of the difficulties raised by the contrast similarity between esophageal GTV and its neighboring tissues in CT scans, this problem has been addressed weakly. In this paper, we present a 3D end-to-end method based on a convolutional neural network (CNN) for this purpose. We leverage design elements from DenseNet in a typical U-shape. The proposed architecture consists of a contractile path and an extending path that includes dense blocks for extracting contextual features and retrieves the lost resolution respectively. Using dense blocks leads to deep supervision, feature re-usability, and parameter reduction while aiding the network to be more accurate. The proposed architecture was trained and tested on a dataset containing 553 scans from 49 distinct patients. The proposed network achieved a Dice value of 0.73±0.20, and a 95% mean surface distance of 3.07±1.86 mm for 85 test scans. The experimental results indicate the effectiveness of the proposed method for clinical diagnosis and treatment systems. © 2018, Springer Nature Switzerland AG
  6. Keywords:
  7. Convolution ; Image segmentation ; Medical computing ; Medical imaging ; Network architecture ; Neural networks ; Tumors ; Computer aided diagnosis(CAD) ; Convolutional neural network ; Convolutional Neural Networks (CNN) ; CT segmentation ; Esophagus ; Gross tumor volume ; Parameter reduction ; Proposed architectures ; Computerized tomography
  8. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 16 September 2018 through 20 September 2018 ; Volume 11073 LNCS , 2018 , Pages 343-351 ; 03029743 (ISSN); 9783030009366 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007%2F978-3-030-00937-3_40