One shot PACS: Patient Specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTS Journal Article

   

Authors:	Jiang, J.; Veeraraghavan, H.
Article Title:	One shot PACS: Patient Specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTS
Abstract:	Image-guided adaptive lung radiotherapy requires accurate tumor and organs segmentation from during treatment cone-beam CT (CBCT) images. Thoracic CBCTs are hard to segment because of low soft-tissue contrast, imaging artifacts, respiratory motion, and large treatment induced intra-thoracic anatomic changes. Hence, we developed a novel Patient-specific Anatomic Context and Shape prior or PACS-aware 3D recurrent registration-segmentation network for longitudinal thoracic CBCT segmentation. Segmentation and registration networks were concurrently trained in an end-to-end framework and implemented with convolutional long-short term memory models. The registration network was trained in an unsupervised manner using pairs of planning CT (pCT) and CBCT images and produced a progressively deformed sequence of images. The segmentation network was optimized in a one-shot setting by combining progressively deformed pCT (anatomic context) and pCT delineations (shape context) with CBCT images. Our method, one-shot PACS was significantly more accurate (p < 0.001) for tumor (DSC of 0.83 ± 0.08, surface DSC [sDSC] of 0.97 ± 0.06, and Hausdorff distance at 95th percentile [HD95] of 3.97±3.02mm) and the esophagus (DSC of 0.78 ± 0.13, sDSC of 0.90±0.14, HD95 of 3.22±2.02) segmentation than multiple methods. Ablation tests and comparative experiments were also done. © 1982-2012 IEEE.
Keywords:	lung tumor; computerized tomography; tumors; patient treatment; biological organs; image segmentation; medical applications; cone-beam ct; computer aided diagnosis; patient specific; recurrent neural networks; anatomic context and shape prior; cbct lung tumor and esophagus segmentation; multi-modality registration; one-shot learning; recurrent network; cone beam ct images; cone-beam ct lung tumor and esophagus segmentation; recurrent networks; shape priors
Journal Title:	IEEE Transactions on Medical Imaging
Volume:	41
Issue:	8
ISSN:	0278-0062
Publisher:	IEEE
Date Published:	2022-08-01
Start Page:	2021
End Page:	2032
Language:	English
DOI:	10.1109/tmi.2022.3154934
PUBMED:	35213307
PROVIDER:	scopus
PMCID:	PMC9642320
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125317526&doi=10.1109%2fTMI.2022.3154934&partnerID=40&md5=0ccd3123069d22f3d5d3ae85f7a26f07
Notes:	Article -- Export Date: 3 October 2022 -- Source: Scopus

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