Introduction of a combination vector to optimise the interpolation of numerical phantoms

Henriet, Julien; Chatonnay, Pascal

doi:10.1016/j.eswa.2012.07.077

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Cordier et al (2006) used Influence functions that link variations in problem descriptors to 145 those in solution descriptors. In the CBR-HS EquiVox, an adaptation based on rules defined by experts' experiences and Artificial Neural Networks (ANN) has been implemented and enhanced by a precision combination vector (Henriet & Chatonnay (2013); Henriet et al (2014a)). In the present study, the neighborhoods of the seeds are explored in order to match the desired grey levels as much 150 as possible.…”

Section: Related Workmentioning

confidence: 99%

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

Florent

Corbat

Chaussy

et al. 2019

Expert Systems with Applications

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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.

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Section: Related Workmentioning

confidence: 99%

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

Florent

Corbat

Chaussy

et al. 2019

Expert Systems with Applications

Self Cite

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“…A. Cordier et al [4] used Influence functions that link variations in problem descriptors to those in solution descriptors. In the CBR-HS EquiVox, an adaptation based on rules defined by experts experiences and Artificial Neural Networks (ANN) has been implemented and enhanced by a precision combination vector [11,12]. In the present work, the neighbourhood of the seeds are explored in order to match as much as possible to desired grey-levels.…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Kidneys Deformed by Nephroblastoma Using Case-Based Reasoning

Florent

Corbat

Delavelle

et al. 2018

Case-Based Reasoning Research and Development

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Image segmentation is a hot topic in image processing research. Most of the time, segmentation is not fully automated, and a user is required to guide the process in order to obtain correct results. Yet, even with programs, it is a time consuming process. In a medical context, segmentation can provide a lot of information to surgeons, but since this task is manual, it is rarely executed because of time,. Artificial Intelligence (AI) is a powerful approach to create viable solutions for a fully automated treatments. In this paper, we defined a Case-Based Reasoning (CBR) which can enhance region-growing segmentation of kidneys deformed by nephroblastoma. The main problem with region-growing methods is a user needs to place the seeds in the image manually. Automated methods exist but are not efficient every time and often give an over-segmentation. That is why we have designed an adaptation phase which can modify the cordinates of seeds recovered during the retrievial phase. We confronted our CBR approach with manual region growing and Convolutional Neural Network (CNN) to segment kidneys and tumors of CT-scans. Our CBR system succeded in performing the best segmentation for the kidney. keywords: Case-Based Reasoning Convolution Neural Network segmentation cancer tumour healthcare imaging artificial intelligence

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