BACKGROUND: The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer\'s disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the \"gold standard\" is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods.
OBJECTIVE: The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms.
METHODS: The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years.
CONCLUSIONS: Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.

Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm\'s principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013-12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi-)genetics. Finally, we highlight points where FreeSurfer-based hippocampal subfield studies may be optimized.

We present a child with Rasmussen encephalitis and highlight the pitfalls of diagnosis when magnetic resonance imaging (MRI) is negative for atrophy. We review the literature regarding this issue, introduce the FreeSurfer software as a potential means of noninvasive diagnosis, and discuss methods for prompt and definitive treatment.
In addition to the patient description, we review the English language literature regarding pathologic diagnosis of Rasmussen encephalitis using the key words Rasmussen encephalitis, focal lesions, MRI, atrophy, epilepsia partialis continua and hemiparesis in PubMed. We conducted a retrospective, volumetric analysis of our patient\'s MRIs using FreeSurfer.
Unlike the majority of patients in the literature with Rasmussen encephalitis, our patient\'s initial MRI was normal and later showed only a small area of T2 and fluid-attenuated inversion recovery high signal despite the presence of epilepsia partialis continua and a rapidly deteriorating clinical course. She did not meet the Rasmussen encephalitis diagnostic criteria until biopsy was obtained but is now seizure-free after functional hemispherotomy performed six months after her initial seizure. FreeSurfer analysis did not show cortical atrophy.
The Bien criteria have poor sensitivity for the diagnosis of Rasmussen encephalitis when the MRI is negative for atrophy. Tissue diagnosis is essential in such instances. We suggest a high clinical index of suspicion and multidisciplinary collaboration between radiology, pathology, and neurosurgery to facilitate a greater emphasis on biopsy followed by hemispherotomy as definitive therapy for individuals with early Rasmussen encephalitis.