Segmentation of deformed kidneys and nephroblastoma using Case-Based Reasoning and Convolutional Neural Network

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 a lot of 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. Yet, a frequent medical constraint is encountered which is a lack of 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 with 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 segmenta- tion. 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, using them, to train a Convolutional Neural Network how to segment. In this article the authors propose an evaluation of a CNN for medical image segmentation following different training sets with avariable number of manual segmentations. To choose slices to train the CNN, an Overlearning Vector for Valid Sparsed 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 tumor per patient with a low 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.

Ensemble U‐net‐based method for fully automated detection and segmentation of renal masses on computed tomography images

“…Gibson et al performed a multiorgan segmentation using a dense V‐Nets algorithm and evaluated their method on CT images of 90 subjects 44 . Lastly, Marie et al introduced a case‐based reasoning approach in which manual region growing and CNN was confronted to delineate deformed kidneys from the CT images 45 . In comparison to previous fully automated methods for kidney boundary segmentation, the main advantage of our method is that it has been evaluated on a relatively large test dataset (185 patients).…”

Conflict management in the fusion of complementary segmentations of deformed kidneys and nephroblastoma

“…Each structure can be calculated using one optimal method of its own in order to obtain its best segmentation. Thus, in the SAIAD project, a method based on CBR and region growing is used in order to segment the kidney deformed by nephroblastoma on each of the patients' 2D images (Marie et al (2018)), and the OV 2 ASSION method, based on CNN, is used to segment nephroblastoma (Marie et al (2019)).…”

Similarity measures and attribute selection for case-based reasoning in transcatheter aortic valve implantation

“…been recently used for medical image processing applications, e.g., to improve kidney tumor segmentation as reported by Marie et al [33].…”

“…Torrent-Fontbona et al [ 32 ] developed a CBR, using a numerical solution as an output rather than predetermined class labels to quantify the bolus insulin dosage. CBR has also been recently used for medical image processing applications, e.g., to improve kidney tumor segmentation as reported by Marie et al [ 33 ].…”