|ECR 2019 / C-2866|
|Cloud-based semi-automated liver segmentation: analytical study to compare its speed and accuracy with a semi-automated workstation based software|
Methods and materials
A test dataset of 15 multi-phasic contrast-enhanced CT scans was provided by Centre for Advanced Research in Imaging, Neurosciences and Genomics (CARING), New Delhi, India.
Exclusion Criteria entailed:
- Morphologic features of cirrhosis,
- History of prior liver/biliary surgery or liver tumor ablation procedures
- One or more liver lesions greater than 3 cm in size identified by CT or MRI
- Portal or hepatic vein thrombosis.
All studies were acquired on a 128-MDCT GE Discovery IQ scanner. The images were acquired using a matrix size of 512 x 512 pixels, at an in-plane pixel size of 0.76 mm, reconstructing 0.6 mm thin images. Individual contrast bolus-tracking was performed during repetitive low dose acquisitions at 120 kVp /40 mAs and placement of a threshold region-of-interest (ROI) within the abdominal aorta at the level of the diaphragm, plotting HU contrast wash-in to a level of 150 HU following contrast administration of 100 ml 320 mg I/ml contrast agent administered at 4 ml / sec injected into a right antecubital vein using a CTA injector. The diagnostic arterial and portal-venous cranio-caudal helical hepatic MDCT acquisition commenced 12 seconds and 60 seconds post 150 HU wash-in, respectively.
Manual vs Automated Liver Volumetry
We performed liver volumetry on two setups (1) A commercially available CT Volume Viewer Package and (2) PredibleLiver (Predible Health, Bengaluru, India), a liver-volumetry software package with segmentations initialized using DNNs (Fig. 2). All quantitative volumetric evaluations were performed by a radiologist (MD) of 7 years of experience. The radiologist performed manual and automated volumetry with an interval of 2 months.
All studies in the test set were loaded on the CT Volumetric Viewer application and made available in axial, saggital and coronal reformations. A seed pointer was centrally placed over internal portions of the liver, with an interactively controlled growing color overlay region-of-interest (ROI) visible to the radiologists; region growing speed (100 mL/sec), seed size (20 mm²) and sensitivity to attenuation differences (sensitivity 5, range 1 – 10) were standardized for the in-vitro phantom and in-vivo patient datasets. If color-overlay ROIs were noticed outside of the liver on axial, sagittal, and coronal reformations, an eraser tool with identical settings was utilized. This was performed until the radiologists deemed the volumetric assessment appropriate. The CT Volume Viewer application was then prompted to provide the whole liver volume.
Venous Phase CT scans of the abdomen were loaded onto PredibleLiver application. The application performs liver segmentation without any user input. PredibleLiver's automated segmentation is based on Deep Neural Networks (DNN). UNet (Fig. 1) is a popular DNN architecture used for medical image segmentation. PredibleLiver uses a 3D UNet architecture to segment liver region in the abdomen venous phase CT. It takes less than a minute to generate the liver segmentation using the neural network. The generated liver segmentation was allowed to modified by the radiologist using region grow and erase tools as described in the previous section. The software was then prompted to provide whole liver volume.
A CNN based on the UNet (Fig. 1) architecture was trained on 324 triphasic contrast-enhanced abdomen CT scans. The data was annotated by radiology technician and the annotations were approved by a radiologist. The protocol for contrast enhancement varied across the dataset. The dataset included images of different slice thickness and pixel spacings. This dataset was independent from the test dataset.
We measured the time taken for performing volumetry on both setups along with the final volumes obtained in order to track consistency with regards to volumetric assessment.
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