Data-efficient knee anatomical landmark localization using deep learning

Abstract. Knee osteoarthritis (OA) is the most common musculoskeletal degenerative disease affecting the joints. OA is examined at a doctor’s visit and an X-ray image is often used to confirm the diagnosis. There is no treatment available for OA, therefore it is important to diagnose knee osteoarthritis at the earliest possible stage to preventpossible complications. Traditional methods used by a practitioners do not detect osteoarthritis as early as possible, therefore other methods are needed for early diagnosis. One possibility is to use novel quantitative imaging biomarkers, computation of which often requires precise understanding of the knee anatomy by a computer. More specifically, it is important to locate different areas of the knee according to anatomical atlases and place relevant regions of interest to compute the imaging biomarkers. A state-of-the-art approach for this problem is based on anatomical landmark localization. In this work, the localization of anatomical landmarks from knee X-rays using deep learning is investigated. To date, statistical methods have been used to localize landmarks, but this work focuses on identification based on deep learning and investigates how the amount of available training data affects performance. The method investigated in the present thesis is based on the KNEEL method developed earlier at the University of Oulu. The aim of this work was to improve this method by adjusting the training parameters and leveraging equivalent regularization for semi-supervised learning. Images from the Osteoarthritis Initiative database were used as material for training and validation. During the work, it was found that by adjusting the parameters used for training, anatomical landmarks can be localized more accurately than in the original KNEEL method. By adding the equivalent regularization, the accuracy of the localization was increased substantially, and a further enhancement in performance can be observed by utilizing unlabeled data in a semi-supervised learning manner. The results, developed in this thesis can layer be leveraged in OA research or even clinical practice, where the computation of quantitative imaging biomarkers is important. To our knowledge, this is the first work in OA where SSL and equivariant regularization were used.Datatehokas polven anatomisten maamerkkien paikantaminen käyttäen syväoppimista. Tiivistelmä. Polven nivelrikko on yleisin niveliin vaikuttava tuki- ja liikuntaelimistöä rappeuttava sairaus. Nivelrikko tutkitaan lääkärikäynnin yhteydessä ja diagnoosi vahvistetaan usein röntgenkuvantamisen avulla. Nivelrikkoon ei ole saatavilla hoitoa, joten on tärkeää diagnosoida polven nivelrikko mahdollisimman varhaisessa vaiheessa mahdollisten komplikaatioiden välttämiseksi. Perinteiset lääkäreiden käyttämät menetelmät eivät tunnista nivelrikkoa riittävän aikaisin, siksi tarvitaan muita menetelmiä varhaisempaan diagnostiikkaan. Yksi mahdollisuus on käyttää kvantitatiivisia kuvantamisbiomarkkereita, mutta näiden laskemiseksi tietokoneen täytyy ymmärtää anatomisia rakenteita tarkasti. Tarkemmin sanottuna on tärkeää paikantaa polven eri rakenteet ihmisen anatomiasta ja merkitä kiinnostavat rakenteet, jotta kuvantamisbiomarkkerit voidaan laskea. Nykyisin tätä ongelmaa lähestytään anatomisten maamerkkien paikantamisen avulla. Tässä työssä tutkittiin anatomisten maamerkkien paikantamista polven röntgenkuvista syväoppimisen avulla. Perinteisesti tähän on käytetty staattisia menetelmiä, mutta tässä työssä keskityttiin paikantamiseen käyttäen syväoppimista ja tutkittiin kuinka käytettävissä oleva opetusdatan määrä vaikuttaa suorituskykyyn. Työssä käytetty metodi perustuu aikaisemmin Oulun yliopistossa kehitettyyn KNEEL metodiin. Tämän työn tarkoituksena oli parantaa tätä metodia säätämällä opetusparametreja sekä hyödyntää ekvivalenttia regularisaatiota syväoppimisen yhteydessa. Kuvia The Osteoarthritis Initiative -tietokannasta käytettiin opetukseen ja validointiin. Työn aikana havaittiin, että säätämällä opetukseen käytettäviä parametrejä, voidaan anatomiset maamerkit paikantaa tarkemmin kuin alkuperäisellä KNEEL metodilla. Ekvivalentin regularisaation lisäämisellä paikantamisen tarkkuus lisääntyi huomattavasti. Suorituskyky parani entisestään käyttämällä annotoimatonta dataa puoli-ohjatun oppimisen yhteydessä. Tämän opinnäytetyön yhteydessä kehitettyä metodia voidaan käyttää nivelrikon tutkimuksen yhteydessä tai kliinisessä käytössä, missä kvantitatiivisten kuvantamisbiomarkkereiden käyttö on tärkeää. Tietojemme mukaan tämä työ on ensimmäinen, jossa käytetään puoliohjattua oppimista sekä ekvivalenttia regularisaatiota nivelrikon yhteydessä

Tiedonhankinnan epävarmuus ja hallinta : opettajaeläkeläisten arkielämän tiedonhankinnan tarkastelua

Summar

Onko internetistä tullut suomalaisten tärkein terveystiedon lähde? Deskriptiivistä tutkimustietoa vuosilta 2001 ja 2009

Jo syntyaikoinaan internetin ennustettiin olevan ylivertainen keskeisten käyttäytymistieteellisten teorioiden ja periaatteiden soveltamiseen terveellisten elämäntapojen edistämisessä. Tämän tutkimuksen tarkoituksena on vertailla internetin terveystiedon käyttöä ja luotettavuutta kahtena ajankohtana, vuosina 2001 ja 2009, ja valottaa sitä, miten tämä verraten nuori sosio-tekninen kulttuuri on kehittynyt vuosituhannen ensimmäisen vuosikymmenen kuluessa. Tutkimuksessa kysytään, ketkä käyttävät tai ovat käyttämättä internetiä terveystiedon lähteenä. Tarkastelu tehdään demografisten tekijöiden sekä koetun terveyden ja itse ilmoitetun painoindeksin (BMI) suhteen. Lisäksi selvitetään sitä, kuinka luotettavina internet-lähteitä pidetään yleisesti sekä verrattuna muihin terveystiedon lähteisiin. Tutkimuksen kohdejoukkona on 18-65 vuoden ikäinen väestö Suomessa

Survey on Techniques for Ontology Interoperability in Semantic Web

Ontology is a shared conceptualization of knowledge representation of particular domain. These are used for the enhancement of semantic information explicitly. It is considered as a key element in semantic web development. Creation of global web data sources is impossible because of the dynamic nature of the web. Ontology Interoperability provides the reusability of ontologies. Different domain experts and ontology engineers create different ontologies for the same or similar domain depending on their data modeling requirements. These cause ontology heterogeneity and inconsistency problems. For more better and precise results ontology mapping is the solution. As their use has increased, providing means of resolving semantic differences has also become very important. Papers on ontology interoperability report the results on different frameworks and this makes their comparison almost impossible. Therefore, the main focus of this paper will be on providing some basics of ontology interoperability and briefly introducing its different approaches. In this paper we survey the approaches that have been proposed for providing interoperability among domain ontologies and its related techniques and tools

Ilmanvaihtojärjestelmien puhtaus ja puhdistaminen sairaaloiden vuodeosastoilla

Ilmanvaihtojärjestelmien epäpuhtaudet voivat auheuttaa sisäilmaongelmia.Työterveyslaitos, Laadukas sisäympäristö -teema, Itä-Suomen yliopisto, Ympäristötieteen laitos, Tampereen ammattikorkeakoulu.1

Evaluation of the Finnish National Biodiversity Action Plan 1997-2005

The results of the evaluation of the Finnish National Biodiversity Action Plan 1997-2005 indicate clear changes towards better consideration of biodiversity in the routines and policies of many sectors of the administration and economy. There are many indications that actors across society have recognized the need to safeguard biodiversity and have begun to adjust their practices accordingly. Several concrete measures have been undertaken in forests, agricultural habitats and in other habitats significantly affected by human activities. Biodiversity research has expanded significantly and the knowledge of Finland´s biological diversity has increased. In general, the Action Plan has supported public discussion of the need to safeguard biodiversity and this discussion has resulted in more positive attitudes towards nature conservation.So far, however, the implemented measures have not been sufficiently numerous or efficient to stop the depletion of original biological diversity. Many habitats remain far from their original state. More species will become endangered in the immediate future unless more effective and far-reaching measuresare taken. The objective of the EU to halt the decline of biodiversity by 2010 will not be achieved given the current development. Although the deterioration in biodiversity may have slowed down in several cases, many economic activities continue to have a negative impact on biodiversity. The scale of these activities is normally greater than that of the measures taken to manage and restore biodiversity.The evaluation focused on detecting changes in the administration of key sectors, analysing the recent development of biodiversity and observing interlinkages between these two. The analysis of administrative measures was based on interviews and on examining policy documents, reports and other relevant literature. The analysis covered changes in the administration of nature conservation, forestry,  agriculture, land use and regional and development cooperation. The analysis of the development of biodiversity was based on employing 75 pressure, state, impact and response indicators. There were 5 to 15 indicators for each of the nine major habitat types of Finland.Three separate case studies were made to provide further insights into some key issues: 1) A GISanalysis was made of the development of land use patterns in North Karelia and south-west Finland between 1990 and 2000, 2) two scenarios on the development of forest structure in North Karelia until 2050 were developed using a special MELA-model and 3) the cost-effectiveness of the agri-environmental support scheme was examined by comparing different land allocation choices and their effects on biodiversity on an average farm in southern Finland. The evaluation also paid special attention to the role of research in safeguarding biodiversity and reflected Finnish experiences against an international background

EuReCa ONE—27 Nations, ONE Europe, ONE Registry A prospective one month analysis of out-of-hospital cardiac arrest outcomes in 27 countries in Europe

AbstractIntroductionThe aim of the EuReCa ONE study was to determine the incidence, process, and outcome for out of hospital cardiac arrest (OHCA) throughout Europe.MethodsThis was an international, prospective, multi-centre one-month study. Patients who suffered an OHCA during October 2014 who were attended and/or treated by an Emergency Medical Service (EMS) were eligible for inclusion in the study. Data were extracted from national, regional or local registries.ResultsData on 10,682 confirmed OHCAs from 248 regions in 27 countries, covering an estimated population of 174 million. In 7146 (66%) cases, CPR was started by a bystander or by the EMS. The incidence of CPR attempts ranged from 19.0 to 104.0 per 100,000 population per year. 1735 had ROSC on arrival at hospital (25.2%), Overall, 662/6414 (10.3%) in all cases with CPR attempted survived for at least 30 days or to hospital discharge.ConclusionThe results of EuReCa ONE highlight that OHCA is still a major public health problem accounting for a substantial number of deaths in Europe.EuReCa ONE very clearly demonstrates marked differences in the processes for data collection and reported outcomes following OHCA all over Europe. Using these data and analyses, different countries, regions, systems, and concepts can benchmark themselves and may learn from each other to further improve survival following one of our major health care events