Multiple resolution residually connected feature streams for automatic lung tumor segmentation from CT images Journal Article

Authors: Jiang, J.; Hu, Y. C.; Liu, C. J.; Halpenny, D.; Hellmann, M. D.; Deasy, J. O.; Mageras, G.; Veeraraghavan, H.
Article Title: Multiple resolution residually connected feature streams for automatic lung tumor segmentation from CT images
Abstract: Volumetric lung tumor segmentation and accurate longitudinal tracking of tumor volume changes from computed tomography images are essential for monitoring tumor response to therapy. Hence, we developed two multiple resolution residually connected network (MRRN) formulations called incremental-MRRN and dense-MRRN. Our networks simultaneously combine features across multiple image resolution and feature levels through residual connections to detect and segment the lung tumors. We evaluated our method on a total of 1210 non-small cell (NSCLC) lung tumors and nodules from three data sets consisting of 377 tumors from the open-source Cancer Imaging Archive (TCIA), 304 advanced stage NSCLC treated with anti- PD-1 checkpoint immunotherapy from internal institution MSKCC data set, and 529 lung nodules from the Lung Image Database Consortium (LIDC). The algorithm was trained using 377 tumors from the TCIA data set and validated on the MSKCC and tested on LIDC data sets. The segmentation accuracy compared to expert delineations was evaluated by computing the dice similarity coefficient, Hausdorff distances, sensitivity, and precision metrics. Our best performing incremental-MRRN method produced the highest DSC of 0.74 ± 0.13 for TCIA, 0.75±0.12 for MSKCC, and 0.68±0.23 for the LIDC data sets. There was no significant difference in the estimations of volumetric tumor changes computed using the incremental-MRRN method compared with the expert segmentation. In summary, we have developed a multi-scale CNN approach for volumetrically segmenting lung tumors which enables accurate, automated identification of and serial measurement of tumor volumes in the lung. © 1982-2012 IEEE.
Keywords: lung cancer; computerized tomography; medical imaging; tumors; lung; detection; segmentation; biological organs; diseases; image segmentation; feature extraction; longitudinal; error detection; cancer; image resolution; deep learning; media streaming; streaming media
Journal Title: IEEE Transactions on Medical Imaging
Volume: 38
Issue: 1
ISSN: 0278-0062
Publisher: IEEE  
Date Published: 2019-01-01
Start Page: 134
End Page: 144
Language: English
DOI: 10.1109/tmi.2018.2857800
PUBMED: 30040632
PROVIDER: scopus
Notes: Export Date: 1 February 2019 -- Source: Scopus
Altmetric Score
MSK Authors
  1. Gikas S Mageras
    248 Mageras
  2. Joseph Owen Deasy
    277 Deasy
  3. Matthew David Hellmann
    163 Hellmann
  4. Yu-Chi Hu
    60 Hu
  5. Jue Jiang
    6 Jiang