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

The fusion of multiple segmentations aims to improve their accuracy in order to make them exploitable. However, conflicts may appear. In this paper, two conflict-management models are proposed for the fusion of complementary segmentations. This conflict-management and fusion procedure, integrated into the SAIAD project, carries out the fusion of deformed kidneys and nephroblastoma using the combination of six independent methods. These methods are based on different criteria, like the adjacent segmented slices, the variation of information, the Dice, the neighbouring labels, the pixel intensity by scanner images, and the fully connected CRFs. The performances of our fusion models was evaluated on 139 scans for three patients with nephroblastoma, and the results demonstrate its effectiveness and the improvement of the resulting segmentations.

Fusion of multiple segmentations of medical images using OV2ASSION and Deep Learning methods: Application to CT-Scans for tumoral kidney.

Nephroblastoma is the most common kidney tumour in children. Its diagnosis is based on imagery. In the SAIAD project, we have designed a platform for optimizing the segmentation of deformed kidney and tumour with a small dataset, using Artificial Intelligence methods. These patient's structures segmented by separate tools and processes must then be fused to obtain a unique numerical 3D representation. However, when aggregating these structures into a final segmentation, conflicting pixels may appear. These conflicts can be solved by IA techniques. This paper presents a synthesis of our segmentation contribution in the SAIAD project and a new fusion method. The segmentation method uses the FCN-8s network with the OV2ASSION training method, which allows segmentation by patient and overcomes the limited dataset. This new fusion method combines the segmentations of the previously performed structures, using a simple and efficient network combined with the OV2ASSION training method as well, in order to manage eventual conflicting pixels. These segmentation and fusion methods were evaluated on pathological kidney and tumour structures of 14 patients affected by nephroblastoma, included in the final dataset of the SAIAD project. They are compared with other methods adapted from the literature. The results demonstrate the effectiveness of our training method coupled with the FCN-8s network in the segmentation process with more patients, and in the case of the fusion process, its effectiveness coupled with a common network, in resolving the conflicting pixels and its ability to improve the resulting segmentations.

3D reconstruction of Wilms' tumor and kidneys in children: Variability, usefulness and constraints.

INTRODUCTION: Wilms' tumor (WT) is the most common type of malignant kidney tumor in children. Three-dimensional reconstructions can be performed pre-operatively to help surgeons in the planning phase. OBJECTIVES: The main objective of this study was to determine the variability of WT segmentation and 3D reconstruction. The secondary objectives were to assess the usefulness of these 3D reconstructions in the surgical planning phase and in the selection of patients for nephron-sparing surgery (NSS). METHODS: 14 scans from 12 patients were manually or semi-automatically segmented by 2 teams using 3D Slicer software. Inter-individual variability of 3D reconstructions was measured based on the Dice index. The utility of 3D reconstructions for the surgical planning was evaluated by 4 pediatric surgeons using a 5-point Likert scale. The possibility of undertaking NSS was evaluated according to the criteria defined in the Umbrella SIOP-RTSG 2016 protocol. RESULTS: Segmentation of the WT, healthy kidney, pathological kidney, arterial and venous vascularization could be performed for all of the patients in this study. Urinary cavities segmentation could only be performed for 5 out of 14 scans that had a delayed acquisition phase. The mean time required to carry out these segmentations was 8.6 h [3-15 h]. The mean Dice index for all of the scans was good (mean: 0.87; range [0.83-0.91]). Considering each anatomical structure, the Dice index was very good for the WT (mean: 0.95; range [0.91-0.97]) and the healthy kidney (mean: 0.95; range [0.93-0.96]), good for the pathological kidney (mean: 0.87; range [0.69-0.96]) and arterial vascularization (mean: 0.84; range [0.74-0.91]). The Dice index was lower than 0.8 for venous vascularization only (mean: 0.77; range [0.58-0.86]). All the surgeons who were interviewed agreed that the 3D reconstructions were realistic representations and useful for the surgical planning phase. The images reconstructed in 3D allowed most of the criteria defined by the Umbrella SIOP-RTSG 2016 protocol to be evaluated regarding the selection of patients who could benefit from NSS. CONCLUSION: The inter-individual variability of 3D reconstructions of WT is acceptable. Three-dimensional representation appears to assist surgeons with the surgical planning phase by allowing them to better anticipate the operative risks. 3D reconstructions can also be an additional tool to better select patients for NSS. However, the manual or semi-automatic method used is very time-consuming, making it difficult for a routinely use. Developing techniques to automate this segmentation process, therefore, appears to be essential if surgeons and radiologists are to use it in daily practice.