  Artificial Intelligence (AI) is a pervasive research topic, permeating
various sectors and applications. In this study, we harness the power of AI,
specifically convolutional neural networks (ConvNets), for segmenting liver
tissues. It also focuses on developing a user-friendly graphical user interface
(GUI) tool, "AI Radiologist", enabling clinicians to effectively delineate
different liver tissues (parenchyma, tumors, and vessels), thereby saving
lives. This endeavor bridges the gap between academic research and practical,
industrial applications. The GUI is a single-page application and is designed
using the PyQt5 Python framework. The offline-available AI Radiologist resorts
to three ConvNet models trained to segment all liver tissues. With respect to
the Dice metric, the best liver ConvNet scores 98.16%, the best tumor ConvNet
scores 65.95%, and the best vessel ConvNet scores 51.94%. It outputs 2D slices
of the liver, tumors, and vessels, along with 3D interpolations in .obj and
.mtl formats, which can be visualized/printed using any 3D-compatible software.
Thus, the AI Radiologist offers a convenient tool for clinicians to perform
liver tissue segmentation and 3D interpolation employing state-of-the-art
models for tissues segmentation. With the provided capacity to select the
volumes and pre-trained models, the clinicians can leave the rest to the AI
Radiologist.
