A multiphase texture-based model of active contours assisted by a convolutional neural network for automatic CT and MRI heart ventricle segmentation

• Published in: Computer methods and programs in biomedicine 2021-11, Vol.211, p.106373-106373, Article 106373
• Main Authors: Carbajal-Degante, Erik, Avendaño, Steve, Ledesma, Leonardo, Olveres, Jimena, Vallejo, Enrique, Escalante-Ramirez, Boris
• Format: Article
• Language: English
• Subjects: Active contours; Convolutional neural network; Heart ventricle segmentation
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2021.106373

•A new approach is presented for fully automated left and right ventricle segmentation in CT and MRI.•The proposal assembles a hybrid method that combines the expertise of a CNN and a novel multiphase active contour model.•A texture embedded stage improves segmentation due to the ability to highlight patterns and enclose similar content within a curve.•The proposed method represents a good alternative for segmentation assessment of limited data set. Background: Left and right ventricle automatic segmentation remains one of the more important tasks in computed aided diagnosis. Active contours have shown to be efficient for this task, however they often require user interaction to provide the initial position, which drives the tool substantially dependent on a prior knowledge and a manual process. Methods: We propose to overcome this limitation with a Convolutional Neural Network (CNN) to reach the assumed target locations. This is followed by a novel multiphase active contour method based on texture that enhances whole heart patterns leading to an accurate identification of distinct regions, mainly left (LV) and right ventricle (RV) for the purposes of this work. Results: Experiments reveal that the initial location and estimated shape provided by the CNN are of great concern for the subsequent active contour stage. We assessed our method on two short data sets with Dice scores of 93% (LV-CT), 91% (LV-MRI), 0.86% (RV-CT) and 0.85% (RV-MRI). Conclusion: Our approach overcomes the performance of other techniques by means of a multiregion segmentation assisted by a CNN trained with a limited data set, a typical issue in medical imaging.