Electroporation-based treatment planning for deep-seated tumors based on automatic liver segmentation of MRI images.
Electroporation is the phenomenon that occurs when a cell is exposed to a high electric field, which causes transient cell membrane permeabilization. A paramount electroporation-based application is electrochemotherapy, which is performed by delivering high-voltage electric pulses that enable the chemotherapeutic drug to more effectively destroy the tumor cells. Electrochemotherapy can be used for treating deep-seated metastases (e.g. in the liver, bone, brain, soft tissue) using variable-geometry long-needle electrodes. To treat deep-seated tumors, patient-specific treatment planning of the electroporation-based treatment is required. Treatment planning is based on generating a 3D model of the organ and target tissue subject to electroporation (i.e. tumor nodules). The generation of the 3D model is done by segmentation algorithms. We implemented and evaluated three automatic liver segmentation algorithms: region growing, adaptive threshold, and active contours (snakes). The algorithms were optimized using a seven-case dataset manually segmented by the radiologist as a training set, and finally validated using an additional four-case dataset that was previously not included in the optimization dataset. The presented results demonstrate that patient's medical images that were not included in the training set can be successfully segmented using our three algorithms. Besides electroporation-based treatments, these algorithms can be used in applications where automatic liver segmentation is required.
University of Ljubljana, Faculty of Electrical Engineering, Ljubljana, Slovenia.
Denis
D
Pavliha
Maja M
MM
Mušič
Gregor
G
Serša
Damijan
D
Miklavčič
D000465:Algorithms; D053672:Electrochemotherapy; D004566:Electrodes; D018274:Electroporation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008099:Liver / Q000000981:diagnostic imaging*; D008279:Magnetic Resonance Imaging; D008962:Models, Theoretical; D009369:Neoplasms / Q000000981:diagnostic imaging / Q000628:therapy*; D010363:Pattern Recognition, Automated; D011859:Radiography