A Deep Learning-Based Automated Framework for Subpeak Designation on Intracranial Pressure Signals.

The intracranial pressure (ICP) signal, as monitored on patients in intensive care units, contains pulses of cardiac origin, where P1 and P2 subpeaks can often be observed. When calculable, the ratio of their relative amplitudes is an indicator of the patient's cerebral compliance. This characterization is particularly informative for the overall state of the cerebrospinal system. The aim of this study is to develop and assess the performances of a deep learning-based pipeline for P2/P1 ratio computation that only takes a raw ICP signal as an input. The output P2/P1 ratio signal can be discontinuous since P1 and P2 subpeaks are not always visible. The proposed pipeline performs four tasks, namely (i) heartbeat-induced pulse detection, (ii) pulse selection, (iii) P1 and P2 designation, and (iv) signal smoothing and outlier removal. For tasks (i) and (ii), the performance of a recurrent neural network is compared to that of a convolutional neural network. The final algorithm is evaluated on a 4344-pulse testing dataset sampled from 10 patient recordings. Pulse selection is achieved with an area under the curve of 0.90, whereas the subpeak designation algorithm identifies pulses with a P2/P1 ratio > 1 with 97.3% accuracy. Although it still needs to be evaluated on a larger number of labeled recordings, our automated P2/P1 ratio calculation framework appears to be a promising tool that can be easily embedded into bedside monitoring devices.

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.

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.