Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.

Major radiation injury is frequent and increases the risk of neurological complications. Its late appearance implies that current follow-up protocols need to be extended in time.

Agreement on the assessment of metastatic spine instability is moderate. The SINS can help improve communication among clinicians in oncology care.

Periprocedural risks and long-term outcomes of patients treated with CAS and presenting a contralateral carotid occlusion does not differ from regular patients treated with CAS. Based on the low stenosis rate of our study, our results do not give credit to extra surveillance measures in patients with contralateral carotid occlusion.

Background and purposeThe purpose was to report the results of ultrasound‐guided lumbar puncture for the administration of nusinersen in spinal muscular atrophy (SMA) patients with complex spines.MethodsEighteen SMA patients (five children, five adolescents and eight adults) with either severe scoliosis or spondylodesis were evaluated for ultrasound‐guided lumbar puncture. Ultrasound was performed with a 3.5 MHz transducer to guide a 22 gauge × 15 mm needle, which was placed in the posterior lumbar space following a parasagittal interlaminar approach.ResultsTwelve patients had undergone spinal instrumentation (nine growing rods and three spinal fusion) whilst the other six showed severe scoliosis. Success was achieved in 91/94 attempts (96.8%), in 14/18 patients (77.8%), including 100% of children and adolescents and 50% of adult patients. In two of the unsuccessfully treated patients, computed tomography and fluoroscopy‐guided transforaminal lumbar punctures were also tried without success. After a median follow‐up of 14 months, only few adverse events, mostly mild, were observed.ConclusionThe ultrasound‐guided lumbar puncture, following an interlaminar parasagittal approach, is a safe and effective approach for intrathecal treatment with nusinersen in children, adolescents and carefully selected adult SMA patients with complex spines and could be considered the first option in them.