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Manual segmentation, which is tedious, time-consuming, and operator-dependent, is currently used as the gold standard to validate automatic and semiautomatic methods that quantify geometries from 2D and 3D MR images. This study examines the accuracy of manual segmentation and generalizes a strategy to eliminate its use. Trained individuals manually measured MR lateral ventricles images of normal and hydrocephalus infants from 1 month to 9.5 years of age. We created 3D-printed models of the lateral ventricles from the MRI studies and accurately estimated their volume by water displacement. MRI phantoms were made from the 3D models and images obtained. Using a previously developed artificial intelligence (AI) algorithm that employs four features extracted from the images, we estimated the ventricular volume of the phantom images. The algorithm was certified when discrepancies between the volumes—gold standards—yielded by the water displacement device and those measured by the automation were smaller than 2%. Then, we compared volumes after manual segmentation with those obtained with the certified automation. As determined by manual segmentation, lateral ventricular volume yielded an inter and intra-operator variation up to 50% and 48%, respectively, while manually segmenting saggital images generated errors up to 71%. These errors were determined by direct comparisons with the volumes yielded by the certified automation. The errors induced by manual segmentation are large enough to adversely affect decisions that may lead to less-than-optimal treatment; therefore, we suggest avoiding manual segmentation whenever possible.

\" the book clearly explains the essential hardware, circuits, and brains and contains easy-to-follow, step-by-step plans for low-cost, cool robotics projects ...\"--Provided by publisher. microcontrollers, and remote control systems.

\"Learn the basics of modern robotics while building your own intelligent robot from scratch! You'll use inexpensive household materials to make the base for your robot, then add motors, power, wheels, and electronics. But wait, it gets better: your creation is actually five robots in one! -- build your bot in stages, and add the features you want. Vary the functions to create a robot that's uniquely yours. Mix and match features to make your own custom robot: Flexible Motorized Base -- a playpen for all kinds of programming experiments Obstacle Detector -- whiskers detect when your robot has bumped into things Object Avoider -- ultrasonic sound lets your robot see what's in front of it Infrared Remote Control -- command your robot from your easy chair Line Follower -- use optics to navigate your bot; have races with other robot builders! You will learn how switches, ultrasonics, infrared detectors, and optical sensors work. Install an Arduino microcontroller board and program your robot to avoid obstacles, provide feedback with lights and sound, and follow a tracking line. In this book you will combine multiple disciplines -- electronics, programming, and engineering -- to successfully build a multifunctional robot. You'll discover how to: construct a motorized base set up an Arduino to function as the brain use \"whisker\" switches to detect physical contact avoid obstacles with ultrasonic sensors teach your robot to judge distances use a universal remote to control your robot install and program a servo motor respond to input with LEDs, buzzers, and tones mount line-following sensors under your robot And more. Everything is explained with lots and lots of full-color line drawings. No prior experience is necessary. You'll have fun while you learn a ton!\" -- ONIX annotation.

The size/volume of the brain’s ventricles is essential in diagnosing and treating many neurological disorders, with various forms of hydrocephalus being some of the most common. Initial ventricular size and changes, if any, in response to disease progression or therapeutic intervention are monitored by serial imaging methods. Significant variance in ventricular size is readily noted, but small incremental changes can be challenging to appreciate. We have previously reported using artificial intelligence to determine ventricular volume. The values obtained were compared with those calculated using the inaccurate manual segmentation as the “gold standard”. This document introduces a strategy to measure ventricular volumes where manual segmentation is not employed to validate the estimations. Instead, we created 3D printed models that mimic the lateral ventricles and measured those 3D models’ volume with a tuned water displacement device. The 3D models are placed in a gel and taken to the magnetic resonance scanner. Images extracted from the phantoms are fed to an artificial intelligence-based algorithm. The volumes yielded by the automation must equal those yielded by water displacement to assert validation. Then, we provide certified volumes for subjects in the age range (1–114) months old and two hydrocephalus patients.

The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that allows analytical capabilities while not perturbing the system's daily operation. Additionally, the strategy is secure and vendor independent. Cerebral ventricular volume is important for the diagnosis and treatment of many neurological disorders. A significant change in ventricular volume is readily recognized, but subtle changes, especially over longer periods of time, may be difficult to discern. Clinical imaging protocols and parameters are often varied making it difficult to use a general solution with standard segmentation techniques. Presented is a segmentation strategy based on an algorithm that uses four features extracted from the medical images to create a statistical estimator capable of determining ventricular volume. When compared with manual segmentations, the correlation was 94% and holds promise for even better accuracy by incorporating the unlimited data available. The volume of any segmentable structure can be accurately determined utilizing the machine learning strategy presented and runs fully automatically within the PACS.

Normal pressure hydrocephalus (NPH) is a poorly understood neurodegenerative condition leading to gait impairment and ultimately dementia. Prior work has shown larger intracranial volume (ICV) among those with NPH which has been taken to establish a link to Benign external hydrocephalus of infancy (BEH) as a predisposing factor. These studies have not evaluated brain volume which we hypothesize will also be elevated in NPH and account for the increase in ICV. Automated analysis was performed on CT head examinations from 305 NPH patients and 294 controls. Brain volume was ~ 4.8% larger in females ( p  < .001) and ~ 2.5% larger in males ( p  = .003) in NPH compared with controls and ICV was ~ 5.2% larger in females ( p  < .001) and ~ 3.7% larger in males ( p  < .001) with NPH compared with controls. The ratio of brain volume to intracranial volume in NPH versus controls was not significantly different for females ( p  = .4) or males, ( p  = .08). If BEH is a major cause of NPH this would then require that it also results in persistently enlarged brain volumes. Our data suggests large brain size itself is a risk factor for NPH and may help account for increased NPH risk among males.

External ventricular drains (EVDs) are used clinically to relieve excess fluid pressure in the brain. However, EVD outflow rate is highly variable and typical clinical flow tracking methods are manual and low resolution. To address this problem, we present an integrated multi-sensor module (IMSM) containing flow, temperature, and electrode/substrate integrity sensors to monitor the flow dynamics of cerebrospinal fluid (CSF) drainage through an EVD. The impedimetric sensors were microfabricated out of biocompatible polymer thin films, enabling seamless integration with the fluid drainage path due to their low profile. A custom measurement circuit enabled automated and portable sensor operation and data collection in the clinic. System performance was verified using real human CSF in a benchtop EVD model. Impedimetric flow sensors tracked flow rate through ambient temperature variation and biomimetic pulsatile flow, reducing error compared with previous work by a factor of 6.6. Detection of sensor breakdown using novel substrate and electrode integrity sensors was verified through soak testing and immersion in bovine serum albumin (BSA). Finally, the IMSM and measurement circuit were tested for 53 days with an RMS error of 61.4 μL/min.

Abstract OBJECTIVE Rathke cleft cysts (RCCs) are cystic epithelial lesions in the sellar and suprasellar regions that are often discovered incidentally. They require surgical fenestration and drainage in a small proportion of patients who develop symptoms or demonstrate progressive enlargement. Our aim was to review our experience with pediatric patients treated surgically for RCCs. METHODS A retrospective review was conducted of all patients treated surgically for RCCs at Childrens Hospital Los Angeles between 1999 and 2007 after approval by the institutional review board. Clinical notes, operative reports, radiological studies, and pathology reports were reviewed. The median follow-up period was 34 months. RESULTS Ten patients undergoing surgical treatment of an RCC were identified, making up 20% of the 51 patients with RCCs followed clinically over the same time period. The mean age was 13 years (age range, 2–17 years). There were 6 females and 4 males. Patients requiring surgery presented with the following clinical symptoms: headache (8 patients, 80%), endocrine insufficiency (6 patients, 60%), meningitis followed by visual loss (1 patient, 10%), and incidental finding (1 patient, 10%). The mean cyst diameter was 13.6 mm (range, 8–18 mm). Four patients had strictly sellar lesions, 4 patients had suprasellar extension of an RCC, and 2 patients had primarily suprasellar RCCs. Nine of 10 patients underwent transsphenoidal surgery, and 1 patient underwent a pterional craniotomy. Complete cyst drainage on radiography was noted in 9 of 10 patients (90%), all of whom underwent transsphenoidal surgery. One patient experienced a symptomatic recurrence 6 years after complete surgical drainage. Headaches improved in 7 of 8 patients after surgery. Two patients had complete resolution of a hormonal axis deficit, whereas 3 patients developed new anterior pituitary axis deficits. Two patients developed persistent diabetes insipidus after surgery. CONCLUSION RCCs are an infrequent cause of symptoms in pediatric patients. The transsphenoidal approach offers an effective means of achieving complete cyst drainage for lesions requiring surgery. Fenestration and aspiration of the cyst are usually sufficient to achieve total resolution of symptoms and signs caused by RCCs. Clinical symptoms such as headaches improved in the majority of patients, whereas hormonal disturbances typically persisted. Patient selection remains of paramount importance when considering surgery for pediatric patients with RCCs.