Open AccessA review of approaches investigated for right ventricular segmentation using short‐axis cardiac MRI
Author(s) -
Ammari Asma,
Mahmoudi Ramzi,
Hmida Badii,
Saouli Rachida,
Bedoui Mohamed Hédi
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12165
Subject(s) - segmentation , hausdorff distance , computer science , dice , artificial intelligence , metric (unit) , machine learning , pattern recognition (psychology) , data mining , mathematics , statistics , engineering , operations management
The right ventricular assessment is crucial to heart disease diagnosis. Unfortunately, its segmentation is quite challenging due to its intricate shape, ill‐defined thin edges, large variability among patients, and pathologies. Besides, it is a very laborious and time‐consuming task to be done manually. Therefore, automated segmentation techniques are very suitable to reduce the strain on the expert. Here, it is attempted to review the taxonomy of the current RV segmentation approaches adopted to handle the afore‐mentioned issues. Enhanced by our expert's interpretation, the results of over forty research papers were evaluated based on several metrics such as the dice metric and the Hausdorff distance. Synthetic tables and charts were also used to discuss the reviewed approaches. The following study shows that none of the existing methods has proved accurate enough to meet all the RV challenging issues. Many misestimated results were reported for several cases. Eventually, global guidance is outlined, which supports combining different methods to enhance the expected results during the MRI short‐axis slice processing.