Liver Injury Clinical Trial
Official title:
Automatic Segmentation by a Convolutional Neural Network (Artificial Intelligence - Deep Learning) of Polycystic Livers, as a Model of Multi-lesional Dysmorphic Livers
Assessing the volume of the liver before surgery, predicting the volume of liver remaining
after surgery, detecting primary or secondary lesions in the liver parenchyma are common
applications that require optimal detection of liver contours, and therefore liver
segmentation.
Several manual and laborious, semi-automatic and even automatic techniques exist.
However, severe pathology deforming the contours of the liver (multi-metastatic livers...),
the hepatic environment of similar density to the liver or lesions, the CT examination
technique are all variables that make it difficult to detect the contours. Current
techniques, even automatic ones, are limited in this type of case (not rare) and most often
require readjustments that make automatisation lose its value.
All these criteria of segmentation difficulties are gathered in the livers of hepatorenal
polycystosis, which therefore constitute an adapted study model for the development of an
automatic segmentation tool.
To obtain an automatic segmentation of any lesional liver, by exceeding the criteria of
difficulty considered, investigators have developed a convolutional neural network
(artificial intelligence - deep learning) useful for clinical practice.
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