Deep learning-based lower back pain classification and detection from T2-weighted magnetic resonance images

Abstract. Lower back pain (LBP) is a common physiological condition that affects 50–80% of the adult population at some point in their lives. For example, the economic load of LBP in Sweden was estimated to be approx. at C740 million in 2011. In LBP diagnostics, magnetic resonance imaging (MRI) is often used. MRI is used to visualize the structures in the lumbar region of the spine such as disks, bones, and spaces between the vertebral bones where nerves pass through. The lumbar spine refers to the lowest five vertebrae and intervertebral discs of the spine. MRI provides a detailed picture of the lumbar spine to get visual confirmation of any abnormalities potentially related to LBP to support the diagnosis process. The goal of this thesis was to investigate visual patterns related to LBP in T2-weighted MR images measured with a fast spin-echo sequence on a GE Healthcare Signa HDxt 1.5 T MRI system. A convolutional neural network was used to classify MRIs into symptomatic and asymptomatic cases and to develop a fully automated pain prediction process. A total of 526 MRI examinations with supporting pain questionnaires from the Northern Finland Birth Cohort 1966 (NFBC1966) were used. Three different datasets were created for the experiments: i) a dataset with mid-sagittal slices from the center of the spine from each examination, ii) a dataset with mid-sagittal slices and its immediate neighboring slices, and similarly, iii) a dataset with five middle-most sagittal slices. In each dataset, individual slices were considered as independent samples, i.e., inputs for the classification method. The developed classification method yielded the best results when the input dataset comprised of three middle-most slices (Balanced Accuracy score (BACC) of 0.709 ± 0.011, Average Precision (AP) of 0.467 ± 0.025, and Area Under Receiver Operating Characteristic curve (ROC-AUC) of 0.740 ± 0.008). The baseline model trained using only the mid-sagittal slice for classification yielded the lowest classification scores (BACC of 0.546 ± 0.032, AP of 0.403 ± 0.007, and ROC-AUC of 0.667 ± 0.008) followed by the model trained with the dataset with five middle-most slices (BACC of 0.675 ± 0.008, AP of 0.369 ± 0.009, and ROCAUC of 0.619 ± 0.011). To conclude, this work suggests that the developed deep learning-based classification pipeline could be used for LBP diagnostics of lumbar spine MRI. LBP diagnostics is heavily based on degenerative MRI findings and deep learning has the potential to supplement these visual assessments objectively. The developed method could be helpful, for example, in identifying negative cases in order to enhance the workflow of routine diagnostic imaging tasks.Alaselkäkivun luokittelu ja havainnointi T2-painotetuista magneettikuvista syväoppimista hyödyntäen. Tiivistelmä. Alaselkäkipu on yleinen fysiologinen tila, joka vaikuttaa 50:stä 80:een %:iin aikuisväestöstä jossain vaiheessa heidän elämäänsä. Ruotsissa alaselkäkipuun liittyvän taloudellisen kuormituksen on arvioitu olleen noin 740 miljoonaa euroa vuonna 2011. Alaselkäkivun syyn etsimiseen käytetään tyypillisesti magneettikuvausta (MRI). MRI:tä käytetään lannerangan alueen rakenteiden, kuten levyjen, luiden ja selkärangan luiden välisten tilojen, joissa hermot kulkevat, visualisoimiseen. Lannerangalla tarkoitetaan selkärangan viittä alinta nikamaa ja levyä. MRI tarjoaa diagnoosin tukemiseksi yksityiskohtaisen kuvan lannerangasta mahdollistaen alaselkäkipuun mahdollisesti liittyvien poikkeamien visuaalisen tarkastelun. Tämän opinnäytetyön tavoitteena oli tutkia alaselkäkipuun liityviä muutoksia T2-painotetuissa magneettikuvissa, jotka kuvattiin GE Healthcare Signa HDxt 1,5 T magneettikuvauslaitteistolla nopeaa spin-kaikusekvenssiä käyttäen. Kuvien luokitteluun käytettiin konvoluutioneuroverkkoja oireellisiin ja oireettomiin tapauksiin täysautomatisen kivun ennustusmenetelmän kehittämiseksi. Aineistona käytettiin yhteensä 526 tutkimusta Pohjois-Suomen syntymäkohortista 1966 (NFBC1966). Testejä varten luotiin kolme erilaista aineistoa: i) keskisagittaaliset viipalekuvat, ii) keskisagittaaliset viipalekuvat ja niiden naapuriviipaleet, sekä vastaavasti iii) viisi keskimmäisintä viipalekuvaa, joita hyödynnettiin itsenäisinä näytteinä, eli luokitusmenetelmän syötteinä. Kehitetty luokitusmenetelmä tuotti parhaat tulokset kun syötejoukkona olivat keskisagittaaliset viipalekuvat ja niiden naapuriviipaleet (Balanced Accuracy score (BACC) 0,709 ± 0,011, Average Precision (AP) 0,467 ± 0,025, ja Area Under Receiver Operating Characteristic curve (ROC-AUC) 0,740 ± 0,008). Keskisagittaalisten viipalekuvien avulla koulutettu vertailumalli tuotti alhaisimmat luokittelutulokset (BACC 0.546 ± 0.032, AP 0.403 ± 0.007, and ROC-AUC 0.667 ± 0.008), ja seuraavaksi paras malli oli viidellä keskimmäisellä viipalekuvalla koulutettu malli (BACC 0.675 ± 0.008, AP 0.369 ± 0.009, and ROC-AUC 0.619 ± 0.011). Tämä työ antaa viitteitä siitä, että syväoppimiseen perustuvaa menetelmää voitaisiin käyttää lannerangan MRI-aineistosta suoritettavaan alaselkäkivun diagnosointiin. Alaselkäkivun diagnostiikka perustuu vahvasti MRIrappeumalöydöksiin, ja syväoppimisella on edellytyksiä täydentää objektiivisella tavalla näitä visuaalisia arvioita. Kehitetystä menetelmästä voisi olla apua esimerkiksi negatiivisten tapausten tunnistamisessa rutiininomaisten diagnostisten kuvantamistehtävien työnkulun tehostamiseksi

Automatic quality assessment in mammography screening:a deep learning based segmentation method

Abstract. Mammography is an imaging method used as a main tool to detect breast cancer at early stages. Images (mammograms) are examined by radiologists, who aim to identify cancerous findings. However, in order to do that, the mammograms need to be of diagnostic quality, which can sometimes be insufficient, and thus the quality of diagnosis also suffers. Radiology technicians (radiographers) are trained to take mammography images, but not in every healthcare center a strict quality control process is established, which may substantially affect the patients. The most common defects in mammograms are positioning defects, which are seen in the images as skin-foldings or non-imaged parts of the breast. The major issue at a process level is that the described positioning issues are noticed late, already at the diagnostic phase. If a radiologist decides that the mammogram is a non-diagnostic quality, the patient needs to revisit the imaging center. If quality control could be automated and standardized, unnecessary patient recalls could be avoided, thus, reducing the costs of the mammographic process. To date, there is a lack of automatic general quality control tools for mammography screening. Looking at the recent advances in artificial intelligence, it may be possible to automate this process. The goal of this thesis was to develop an automatic system for quality assessment of mammograms. The author used Deep learning to develop an automatic framework for automatic segmentation of defects in mammograms using a dataset of 512 mammographic images extracted from the Oulu University Hospital archive. The second stage of the developed method performed quality assessment by analyzing the presence and location of different tissues in the images from the predicted segmentations. The developed segmentation model yielded a Dice coefficient over 0.90 for the whole breast, breast, and pectoral muscle, and over 0.60 for skin-foldings and nipple. The developed method is the first to tackle automatic segmentation of all major positioning issues in mammography. Ultimately, the developed technology has a potential to improve the mammography workflows and, eventually, patient outcomes.Automaattinen laadunarviointi mammografian kuvauksessa : syväoppimispohjainen segmentointimenetelmä. Tiivistelmä. Mammografiaa on kuvantamismenetelmä, jota käytetään päävälineenä rintasyövän havaitsemiseksi varhaisessa vaiheessa. Radiologien on tutkittava mammogrammit ja päätettävä sitten, onko pahanlaatusia löydöksiä, ja tätä varten mammografiakuvien on oltava diagnostisesti laadukkaita. Ammattilaiset koulutetaan mammografiakuvien ottamiseksi, mutta ei kaikissa terveyskeskuksissa on otettu käyttöön tiukka laadunvalvontaprosessi, joka voi vaikuttaa merkittävästi potilaisiin. Kuvissa voi olla virheitä, jotka tekevät kuvista ei-diagnostisen laadukkaan mammogrammin, ja ne voivat vaikuttaa diagnostiikkatuloksiin. Yksi näistä vioista ovat paikannusvirheet, joissa näkyvät kuvissa ihon taitoksina ja jotkut rinnan osat eivät näy. Suurin ongelma prosessitasolla on, että kuvatut paikannusvirheet havaitaan myöhässä, jo diagnoosivaiheessa. Jos radiologit päättävät, että mammografiakuva ei ole diagnostisesti laadukas, potilaan on palattava kuvantamiskeskukseen ja tutkittava uudelleen, mikä voi lisätä kustannuksia ja työmäärää. Jos laadunvalvonta voidaan automatisoida ja standardoida, voidaan välttää tarpeetonta potilaan palauttamista ja vähentää siten mammografiaprosessin kustannuksia. Tähän mennessä mammografiaseulonnassa ei ole automaattista yleistä laadunvalvontaa. Kun tarkastellaan tekoälyn viimeaikaisia edistystä, tämän prosessin automatisointi voi olla mahdollista. Tämän projektin tarkoituksena oli todistaa diagnostisten ja ei-diagnostisten laatumammogrammien automaattisen erottamisen toteutettavuus. Kirjoittaja käytti syvää oppimista automatisoidun kehyksen luomisessa käyttämällä 512 mammografiakuvaa, jotka otettiin Oulun yliopistollisen sairaalan arkistosta. Automaattisen menetelmän ensimmäisessä vaiheessa suoritettiin rintakudosten ja ihon taittumien segmentointi. Toisessa vaiheessa suoritettiin laadunarviointi analysoimalla eri kudosten läsnäolo ja sijainti kuvissa. Kehitetyllä segmentointimallilla saavutettiin merkittäviä tuloksia, kun koko rinnan ja rintalihasten segmentoinnin onnistumisen hyvyttä mittaava Dice-kerroin oli yli 0,90, ja ihon taittumiselle ja nännille yli 0,60. Kehitetty menetelmä on ensimmäinen, joka käsittelee mammografian kaikkien tärkeimpien paikannusvirheiden automaattista segmentointia. Sillä on potentiaalia myötävaikuttaa mammografian työnkulkujen ja potilastulosten parantamiseen

A point symmetry based method for transforming ODEs with three-dimensional symmetry algebras to their canonical forms

We provide an algorithmic approach to the construction of point transformations for scalar ordinary differential equations that admit three-dimensional symmetry algebras which lead to their respective canonical forms

Workers Attitudes in the Municipalities and Provinces towards the Decentralized and Regional Project in Jordan

The present study aimed to identify the attitudes of workers in local Jordanian public departments as a project towards decentralization of territories of its municipalities in Jordan, The study sample consist of (228) managers, and employees of different ranks whom are working in municipalities and local governmental departments, in order to answer the study questions and hypotheses, the researcher developed questionnaires to measure main variables of the study. Some statistical techniques were used for testing the hypotheses and answering the study questions. The present study revealed the following findings: There is statistically significant correlation between decentralization advantages and environmental factors.There are no statistically significant impact of investigators attitudes toward advantages and disadvantages of decentralization according to demographic factors.There is statistically significant impact of scientific qualification attitudes toward decentralization disadvantages.There is statistically significant impact of investigators attitudes toward decentralization disadvantages and advantages. Keywords: decentralization, attitudes, territories, project in Jordan, environmental factors Participation, political factors, social factors, financial, Developmental planning Factors, organizational factors

Implementing Lean Management Techniques at a Radiation Oncology Department

Objectives: Lean management (LM) principles were first developed by a Japanese manufacturing company to maximise value and minimise waste in the automotive industry. However, these principles can also be applied in the healthcare sector. This study aimed to illustrate the process of implementing LM principles in a radiation oncology department to streamline workflow and identify and reduce waste. Methods: This study took place in the Department of Radiation Oncology, Royal Hospital, Muscat, Oman, in December 2016. A value stream map (VSM) was created for the chain of processes followed in the department. A waste analysis was conducted to determine which processes did not add value for the patient or healthcare provider. Results: Based on the VSM analysis, only six out of 13 steps were found to be of value. Necessary and unnecessary non-value-adding activities were identified. Sources of waste included parking and registration. In addition, variabilities in workload were noted. Conclusion: Overall, LM principles improve workflow, reduce waste and enhance patient and staff satisfaction. In the current study, the application of LM principles helped to improve value in a radiation oncology department.Keywords: Health Services Administration; Healthcare Quality Assurance; Total Quality Management; Organization and Administration; Efficiency; Oman

Synthesis of components to form linked multi-metal constructs : potential MRI/optical contrast agents

Azamacrocyclic chelators with coordinating pendant arms can form complexes with lanthanide(lll) ions offering a high level of kinetic and thermodynamic stability. There are many potentially useful applications of these complexes in areas such as magnetic resonance imaging (MRI) contrast agents, or luminescent imaging agents dependent on the incorporated metal centre (possible examples include gadolinium(lll), europium(lll) or terbium(lll)).The synthesis and characterisation of multi-metallic metal complexes as potential imaging agents have been carried out. A series of novel chelators designed to coordinate lanthanide ions (Gd³⁺, Eu³⁺, Tb³⁺, y³⁺) based on the known D03A chelator have been synthesised bearing various heterocyclic pendent arms (pyridine, pyrazine, pyrimidine), which can act as chromophore groups or as linking units to form multi-metallic species. NMR and potentiometric titrations of the ligands and the lanthanide complexes have been investigated to determine the protonation states across a pH range. The relaxometric properties have been studied for gadolinium(lll) complexes of the synthesised chelators showing high relaxivity rates for both T1 (up to 51.8 mM⁻¹ s⁻¹) and T2 (up to 21.6 mM⁻¹ s⁻¹)

AFFECTING FACTORS ON THE TIMING OF THE ISSUANCE OF ANNUAL FINANCIAL REPORTS "EMPIRICAL STUDY ON THE JORDANIAN PUBLIC SHAREHOLDING COMPANIES"

The study aims to investigate the effect of several factors (company’s size represented by total assets, earnings per share, return on equity, return on assets, dividends per share, company’s age, cash flows from operating activities, in addition to the financial leverage of the company) on the timing of the issuance of the annual financial reports. The sample consists of (120) Jordanian public shareholding companies listed in Amman Stock Exchange (ASE) in 2012, and to achieve the objectives of the study, The study developed model by using multiple regression which included many independent variables and the timing of issuance of annual financial reports as the dependent variable.The results showed that Jordanian companies listed on the ASE delayed in reporting the annual financial period amounted to (111) days from the end of the fiscal year, and that the industrial companies need more time to issue public reports to become compared with other sectors. The results of multiple regression analysis showed also a positive correlation statistically significant between the company’s size, company’s age, financial leverage and the timing of annual financial reports of companies. There is a negative correlation statistical significance between earnings per share and the timing of issuance of the annual financial reports companies