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Comparison of interactive and automatic segmentation of stereoelectroencephalography electrodes on computed tomography post-operative images: preliminary results

Abstract : Stereoelectroencephalography is a surgical procedure used in the treatment of pharmacoresistant epilepsy. Multiple electrodes are inserted in the patient's brain in order to record the electrical activity and detect the epileptogenic zone at the source of the seizures. An accurate localisation of their contacts on post-operative images is a crucial step to interpret the recorded signals and achieve a successful resection afterwards. In this Letter, the authors propose interactive and automatic methods to help the surgeon with the segmentation of the electrodes and their contacts. Then, they present a preliminary comparison of the methods in terms of accuracy and processing time through experimental measurements performed by two users, and discuss these first results. The final purpose of this work is to assist the neurosurgeons and neurologists in the contacts localisation procedure, make it faster, more precise and less tedious. 1. Introduction: Pharmacoresistant epilepsy is a complicated illness that can sometimes be curable by resorting to a surgical intervention, after performing a stereoelectroencephalography (SEEG) to detect the seizure onset zone [1]. SEEG procedure consists of inserting electrodes in the patient's brain to record the electrical activity within the brain parts that are likely to be responsible for the seizures. After this intervention, a post-operative computed tomography (CT) image is acquired to assess if the electrodes and their recording contacts are placed as planned. The localisation of electrodes contacts is a crucial step in the post-SEEG phase since the next surgical intervention aiming at destroying the epileptogenic zone by performing a radio-frequency thermocoagulation [2] or by excising that zone is based on its accurate localisation. However, the contacts localisation and identification is difficult and time-consuming, especially when the neurosurgeon has to process a large number of slices, and there is a high number of implanted electrodes. To help the surgeon with this task, a limited number of groups have recently started to investigate the automatic segmentation of SEEG [3, 4]. A comparison with their works is proposed in the discussion. Other studies have been proposed for deep brain stimulation [5] electrodes, with a lower number of contacts and electrodes involved in the process. In this Letter, we present an automatic segmentation algorithm that we have developed. It takes post-operative CT images as an input (an example of these images is given in Fig. 1 below), then performs a succession of processing steps in order to detect the different electrodes and provide the surgeons with the location of the contact. We have also implemented two versions of simple and intuitive interactive segmentation processes that require minimal interaction from the surgeon. The objective is twofold: the results of these methods will be used as a reference for the proposed automatic segmentation to be validated and will be compared with assessing the interest of automatic methods. In the next sections, we will expose the principle of each of the interactive and automatic seg-mentations, preliminary results obtained in each case and a first comparison between the methods. Finally, we will conclude this Letter by briefly discussing the performance and the limitations
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Sahar Benadi, Irene Ollivier, Caroline Essert. Comparison of interactive and automatic segmentation of stereoelectroencephalography electrodes on computed tomography post-operative images: preliminary results. Healthcare Technology Letters, The Institution of Engineering and Technology, 2018, 5 (5), pp.215-220. ⟨10.1049/htl.2018.5070⟩. ⟨hal-02354320⟩

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