Segmentation of deformed kidneys and nephroblastoma using Case-Based Reasoning and Convolutional Neural Network. (1st August 2019)

• Record Type: Journal Article
• Authors: Marie, Florent; Corbat, Lisa; Chaussy, Yann; Delavelle, Thibault; Henriet, Julien; Lapayre, Jean-Christophe
• Is Part Of: Expert systems with applications. Volume 127(2019)
• Page Start: 282
• Page End: 294
• Publication Date: 2019-08-01
• Subjects: Case-based reasoning -- Convolutional neural networks -- Image segmentation -- Tumor -- Healthcare imaging -- Deep learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.03.010
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: This paper suggests two methods based on AI tools for medical image segmentation. The first method uses CBR to guide a region-growing process for segmentation. The second method based on Deep Learning performs segmentation with a small data set. Both methods were tested on CT-scans images from children with tumoral kidneys . They provided accurate enough results to consider an automatic. segmentation Abstract: Most often, image segmentation is not fully automated and a user is required to lead the process in order to obtain correct results. In a medical context, segmentation can furnish much information to surgeons, but this task is rarely executed. Artificial Intelligence (AI) is a powerful approach for devising a viable solution to fully automated treatment. In this paper, we have focused on kidneys deformed by nephroblastoma. However, a frequent medical constraint is encountered which is a lack of sufficient data with which to train our system. In function of this constraint, two AI approaches were used to segment these structures. First, a Case Based Reasoning (CBR) approach was defined which can enhance the growth of regions for segmentation of deformed kidneys using an adaptation phase to modify coordinates of recovered seeds. This CBR approach was confronted with manual region growing and a Convolutional Neural Network (CNN). The CBR system succeeded in performing the best segmentation for the kidney with a mean Dice of 0.83. Deep Learning was then examined as a possible solution, using the latest performing networks for image segmentation. However, for relevant efficiency, this method requires a large data set. An option would be to manually segment only certain representative slices from a patient and then use them to train a Convolutional Neural Network (CNN) how to segment. In this article the authors propose an evaluation of a CNN for medical image segmentation following different training sets with a variable number of manual segmentations. To choose slices to train the CNN, an Overlearning Vector for Valid Sparse SegmentatIONs (OV 2 ASSION) was used, with the notion of gap between two slices from the training set. This protocol made it possible to obtain reliable segmentations of per patient with a small data set and to determine that only 26% of initial segmented slices are required to obtain a complete segmentation of a patient with a mean Dice of 0.897.