Περίληψη:
Background: COVID-19 lung segmentation using Computed Tomography (CT)
scans is important for the diagnosis of lung severity. The process of
automated lung segmentation is challenging due to (a) CT radiation
dosage and (b) ground-glass opacities caused by COVID-19. The lung
segmentation methodologies proposed in 2020 were semi- or automated but
not reliable, accurate, and user-friendly. The proposed study presents a
COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint (TM),
Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for
lung segmentation. Methodology: The COVLIAS 1.0 consists of three
methods based on solo deep learning (SDL) or hybrid deep learning (HDL).
SegNet is proposed in the SDL category while VGG-SegNet and
ResNet-SegNet are designed under the HDL paradigm. The three proposed AI
approaches were benchmarked against the National Institute of Health
(NIH)-based conventional segmentation model using fuzzy-connectedness. A
cross-validation protocol with a 40:60 ratio between training and
testing was designed, with 10% validation data. The ground truth (GT)
was manually traced by a radiologist trained personnel. For performance
evaluation, nine different criteria were selected to perform the
evaluation of SDL or HDL lung segmentation regions and lungs long axis
against GT. Results: Using the database of 5000 chest CT images (from 72
patients), COVLIAS 1.0 yielded AUC of similar to 0.96, similar to 0.97,
similar to 0.98, and similar to 0.96 (p-value < 0.001), respectively
within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and
NIH. The mean Figure of Merit using four models (left and right lung)
was above 94%. On benchmarking against the National Institute of Health
(NIH) segmentation method, the proposed model demonstrated a 58% and
44% improvement in ResNet-SegNet, 52% and 36% improvement in
VGG-SegNet for lung area, and lung long axis, respectively. The PE
statistics performance was in the following order: ResNet-SegNet >
VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image.
Conclusions: The COVLIAS 1.0 system can be applied in real-time for
radiology-based clinical settings.
Συγγραφείς:
Suri, Jasjit S.
Agarwal, Sushant
Pathak, Rajesh
Ketireddy,
Vedmanvitha
Columbu, Marta
Saba, Luca
Gupta, Suneet K. and
Faa, Gavino
Singh, Inder M.
Turk, Monika
Chadha, Paramjit S.
and Johri, Amer M.
Khanna, Narendra N.
Viskovic, Klaudija and
Mavrogeni, Sophie
Laird, John R.
Pareek, Gyan
Miner, Martin
and Sobel, David W.
Balestrieri, Antonella
Sfikakis, Petros P.
and Tsoulfas, George
Protogerou, Athanasios
Misra, Durga
Prasanna
Agarwal, Vikas
Kitas, George D.
Teji, Jagjit S. and
Al-Maini, Mustafa
Dhanjil, Surinder K.
Nicolaides, Andrew and
Sharma, Aditya
Rathore, Vijay
Fatemi, Mostafa
Alizad, Azra
and Krishnan, Pudukode R.
Frence, Nagy
Ruzsa, Zoltan
Gupta,
Archna
Naidu, Subbaram
Kalra, Mannudeep
