Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma.

PURPOSE: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. METHODS AND MATERIALS: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 ± 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross-validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. RESULTS: We obtained a DSC of 94.90 ± 2.12% for the sclera and the cornea, 94.72 ± 1.89% for the vitreous humor, and 85.16 ± 4.91% for the lens. The mean distance error was 0.26 ± 0.09 mm. The entire process took 14 seconds on average per eye. CONCLUSION: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.

A Segmentation Model for Hi-tech Business Markets

Segmentointi on strateginen työkalu, joka tehostaa yrityksen resurssien käyttöä ja siten vaikuttaa kaikkiin asiakkuuksiin liittyviin liiketoimintaprosesseihin. Työn tavoitteena oli muodostaa segmentointimalli (sisältää sekä segmentointiprosessin että kriteerit) yritysinternetmarkkinoille. Työn tuloksia voidaan kuitenkin tulkita ja soveltaa laajemmin korkean teknologian yrityspalvelumarkkinoille. Tämä tutkielma lisää tietämystämme ja tarjoaa uudenlaisen näkemyksen segmentointiin korkean teknologian yrityspalvelumarkkinoilla. Työssä kuvataan korkean teknologian ja yritys- sekä palvelumarkkinoinnin erityispiirteitä ja kuinka nämä tekijät vaikuttavat segmentointimallin. Tutkimuksessa selvitettiin kohdeyrityksen nykyiset segmentointikäytännöt henkilökohtaisin asiantuntijahaastatteluin. Haastatteluiden avulla luotiin kuva nykyisistä lähestymistavoista sekä niiden lähtökohdista, vahvuuksista ja haasteista. Haastatteluiden analysoinnin jälkeen perustettiin projekti segmentoinnin kehittämiseksi. Työ tuloksena luotiin segmentointimalli, joka tarjoaa vankan perustan segmentoinnin kehittämiselle jatkuvana prosessina. Työssä esitetään segmentoinnin integroimista yrityksen asiakkuuksiin liittyviin liiketoimintaprosesseihin, joka usein puuttuu aiemmista töistä, sekä informaationkulun tehostamista segmentoinnin hyödyntämiseksi tehokkaammin. Segmentointi on strateginen työkalu ja vaatii siksi ylemmän johdon tuen ja sitoutumisen. Oikein sovellettuna segmentointi tarjoaa liiketoiminnalle mahdollisuuden merkittäviin etuihin kuten asiakastyytyväisyyden ja kannattavuuden kehittämiseen.

Standardized evaluation methodology and reference database for evaluating IVUS image segmentation

This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated. We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be solved.

Exploring Different Types of Sharing: A Proposed Segmentation of the Market for "Sharing Businesses"

Sharing instead of buying is regaining traction among today's consumers. This study aims at identifying segments of sharing consumers to unearth potentially viable clusters of a consumer behavior that is a market of growing economic relevance. By means of a qualitative study and a survey with a roughly representative sample of 1121 Swiss-German and German consumers, a set of trait-related, motivational, and perceived socioeconomic variables is identified that can be used to group individuals into segments that differ with regard to their approach to sharing. A cluster analysis based on these variables suggests four potential clusters of sharing consumers-sharing idealists, sharing opponents, sharing pragmatists, and sharing normatives. Two sets of testable propositions are derived that can guide further research in this domain and pave the way to a more targeted approach to the growing market of "sharing" businesses.

Segmentation model for small and medium size business sector: Case Oy Compaq Computer Ab

Markkinasegmentointi nousi esiin ensi kerran jo 50-luvulla ja se on ollut siitä lähtien yksi markkinoinnin peruskäsitteistä. Suuri osa segmentointia käsittelevästä tutkimuksesta on kuitenkin keskittynyt kuluttajamarkkinoiden segmentointiin yritys- ja teollisuusmarkkinoiden segmentoinnin jäädessä vähemmälle huomiolle. Tämän tutkimuksen tavoitteena on luoda segmentointimalli teollismarkkinoille tietotekniikan tuotteiden ja palveluiden tarjoajan näkökulmasta. Tarkoituksena on selvittää mahdollistavatko case-yrityksen nykyiset asiakastietokannat tehokkaan segmentoinnin, selvittää sopivat segmentointikriteerit sekä arvioida tulisiko tietokantoja kehittää ja kuinka niitä tulisi kehittää tehokkaamman segmentoinnin mahdollistamiseksi. Tarkoitus on luoda yksi malli eri liiketoimintayksiköille yhteisesti. Näin ollen eri yksiköiden tavoitteet tulee ottaa huomioon eturistiriitojen välttämiseksi. Tutkimusmetodologia on tapaustutkimus. Lähteinä tutkimuksessa käytettiin sekundäärisiä lähteitä sekä primäärejä lähteitä kuten case-yrityksen omia tietokantoja sekä haastatteluita. Tutkimuksen lähtökohtana oli tutkimusongelma: Voiko tietokantoihin perustuvaa segmentointia käyttää kannattavaan asiakassuhdejohtamiseen PK-yritys sektorilla? Tavoitteena on luoda segmentointimalli, joka hyödyntää tietokannoissa olevia tietoja tinkimättä kuitenkaan tehokkaan ja kannattavan segmentoinnin ehdoista. Teoriaosa tutkii segmentointia yleensä painottuen kuitenkin teolliseen markkinasegmentointiin. Tarkoituksena on luoda selkeä kuva erilaisista lähestymistavoista aiheeseen ja syventää näkemystä tärkeimpien teorioiden osalta. Tietokantojen analysointi osoitti selviä puutteita asiakastiedoissa. Peruskontaktitiedot löytyvät mutta segmentointia varten tietoa on erittäin rajoitetusti. Tietojen saantia jälleenmyyjiltä ja tukkureilta tulisi parantaa loppuasiakastietojen saannin takia. Segmentointi nykyisten tietojen varassa perustuu lähinnä sekundäärisiin tietoihin kuten toimialaan ja yrityskokoon. Näitäkään tietoja ei ole saatavilla kaikkien tietokannassa olevien yritysten kohdalta.

Expert consensus document: European Consensus Statement on congenital hypogonadotropic hypogonadism-pathogenesis, diagnosis and treatment.

Congenital hypogonadotropic hypogonadism (CHH) is a rare disorder caused by the deficient production, secretion or action of gonadotropin-releasing hormone (GnRH), which is the master hormone regulating the reproductive axis. CHH is clinically and genetically heterogeneous, with >25 different causal genes identified to date. Clinically, the disorder is characterized by an absence of puberty and infertility. The association of CHH with a defective sense of smell (anosmia or hyposmia), which is found in ∼50% of patients with CHH is termed Kallmann syndrome and results from incomplete embryonic migration of GnRH-synthesizing neurons. CHH can be challenging to diagnose, particularly when attempting to differentiate it from constitutional delay of puberty. A timely diagnosis and treatment to induce puberty can be beneficial for sexual, bone and metabolic health, and might help minimize some of the psychological effects of CHH. In most cases, fertility can be induced using specialized treatment regimens and several predictors of outcome have been identified. Patients typically require lifelong treatment, yet ∼10-20% of patients exhibit a spontaneous recovery of reproductive function. This Consensus Statement summarizes approaches for the diagnosis and treatment of CHH and discusses important unanswered questions in the field.

Ultra-high Field (7 T) MRI and a Novel Robust Segmentation of Thalamic Nuclei on DWI for Gamma Knife Surgery Purposes: The Targeting of the Ventrointermediate Nucleus

Introduction: Gamma Knife surgery (GKS) is a noninvasive neurosurgical stereotactic procedure, increasingly used as an alternative to open functional procedures. This includes the targeting of the ventrointermediate nucleus of the thalamus (e.g., Vim) for tremor. Objective: To enhance anatomic imaging for Vim GKS using high-field (7 T) MRI and Diffusion Weighted Imaging (DWI). Methods: Five young healthy subjects and two patients were scanned both on 3 and 7 T MRI. The protocol was the same in all cases, and included: T1-weighted (T1w) and DWI at 3T; susceptibility weighted images (SWI) at 7T for the visualization of thalamic subparts. SWI was further integrated into the Gamma Plan Software® (LGP, Elekta Instruments, AB, Sweden) and co-registered with 3T images. A simulation of targeting of the Vim was done using the quadrilatere of Guyot. Furthermore, a correlation with the position of the found target on SWI and also on DWI (after clustering of the different thalamic nuclei) was performed. Results: For the 5 healthy subjects, there was a good correlation between the position of the Vim on SWI, DWI and the GKS targeting. For the patients, on the pretherapeutic acquisitions, SWI helped in positioning the target. For posttherapeutic sequences, SWI supposed position of the Vim matched the corresponding contrast enhancement seen at follow-up MRI. Additionally, on the patient's follow-up T1w images, we could observe a small area of contrast-enhancement corresponding to the target used in GKS (e.g., Vim), which belongs to the Ventral-Lateral-Ventral (VLV) nuclei group. Our clustering method resulted in seven thalamic groups. Conclusion: The use of SWI provided us with a superior resolution and an improved image contrast within the central gray matter, enabling us to directly visualize the Vim. We additionally propose a novel robust method for segmenting the thalamus in seven anatomical groups based on DWI. The localization of the GKS target on the follow-up T1w images, as well as the position of the Vim on 7 T, have been used as a gold standard for the validation of VLV cluster's emplacement. The contrast enhancement corresponding to the targeted area was always localized inside the expected cluster, providing strong evidence of the VLV segmentation accuracy. The anatomical correlation between the direct visualization on 7T and the current targeting methods on 3T (e.g., quadrilatere of Guyot, histological atlases, DWI) seems to show a very good anatomical matching.

Automatic Brain Extraction in Fetal MRI using Multi-Atlas-based Segmentation

In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality.

Investigating the functions of subregions within anterior hippocampus.

Previous functional MRI (fMRI) studies have associated anterior hippocampus with imagining and recalling scenes, imagining the future, recalling autobiographical memories and visual scene perception. We have observed that this typically involves the medial rather than the lateral portion of the anterior hippocampus. Here, we investigated which specific structures of the hippocampus underpin this observation. We had participants imagine novel scenes during fMRI scanning, as well as recall previously learned scenes from two different time periods (one week and 30 min prior to scanning), with analogous single object conditions as baselines. Using an extended segmentation protocol focussing on anterior hippocampus, we first investigated which substructures of the hippocampus respond to scenes, and found both imagination and recall of scenes to be associated with activity in presubiculum/parasubiculum, a region associated with spatial representation in rodents. Next, we compared imagining novel scenes to recall from one week or 30 min before scanning. We expected a strong response to imagining novel scenes and 1-week recall, as both involve constructing scene representations from elements stored across cortex. By contrast, we expected a weaker response to 30-min recall, as representations of these scenes had already been constructed but not yet consolidated. Both imagination and 1-week recall of scenes engaged anterior hippocampal structures (anterior subiculum and uncus respectively), indicating possible roles in scene construction. By contrast, 30-min recall of scenes elicited significantly less activation of anterior hippocampus but did engage posterior CA3. Together, these results elucidate the functions of different parts of the anterior hippocampus, a key brain area about which little is definitely known.