Development of an open source software module for enhanced visualization during MR-guided interstitial gynecologic brachytherapy

In 2010, gynecologic malignancies were the 4th leading cause of death in U.S. women and for patients with extensive primary or recurrent disease, treatment with interstitial brachytherapy may be an option. However, brachytherapy requires precise insertion of hollow catheters with introducers into the tumor in order to eradicate the cancer. In this study, a software solution to assist interstitial gynecologic brachytherapy has been investigated and the software has been realized as an own module under (3D) Slicer, which is a free open source software platform for (translational) biomedical research. The developed research module allows on-time processing of intra-operative magnetic resonance imaging (iMRI) data over a direct DICOM connection to a MR scanner. Afterwards follows a multi-stage registration of CAD models of the medical brachytherapy devices (template, obturator) to the patient's MR images, enabling the virtual placement of interstitial needles to assist the physician during the intervention.Comment: 9 pages, 6 figure

A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning

Objective: Manual contouring and registration for radiotherapy treatment planning and online adaptation for cervical cancer radiation therapy in computed tomography (CT) and magnetic resonance images (MRI) are often necessary. However manual intervention is time consuming and may suffer from inter or intra-rater variability. In recent years a number of computer-guided automatic or semi-automatic segmentation and registration methods have been proposed. Segmentation and registration in CT and MRI for this purpose is a challenging task due to soft tissue deformation, inter-patient shape and appearance variation and anatomical changes over the course of treatment. The objective of this work is to provide a state-of-the-art review of computer-aided methods developed for adaptive treatment planning and radiation therapy planning for cervical cancer radiation therapy. Methods: Segmentation and registration methods published with the goal of cervical cancer treatment planning and adaptation have been identified from the literature (PubMed and Google Scholar). A comprehensive description of each method is provided. Similarities and differences of these methods are highlighted and the strengths and weaknesses of these methods are discussed. A discussion about choice of an appropriate method for a given modality is provided. Results: In the reviewed papers a Dice similarity coefficient of around 0.85 along with mean absolute surface distance of 2-4. mm for the clinically treated volume were reported for transfer of contours from planning day to the treatment day. Conclusions: Most segmentation and non-rigid registration methods have been primarily designed for adaptive re-planning for the transfer of contours from planning day to the treatment day. The use of shape priors significantly improved segmentation and registration accuracy compared to other models

Further Development of a Diagnostic Tool for Autodelineation of Cervical Cancers in MR Images

Svulstavgrensning og svulstinntegning innenfor medisinsk avbildning er en utfordrende, tid-krevende og stadig mer kompleks del av strålebehandling. I denne oppgaven videreutvikles et program som automatiserer svulstinntegningen, basert på MR-bilder av pasienter med livmorhalskreft. Programmet klassifiserer vokslene i MR-bildene som svulst eller ikke-svulst, med metoden lineær diskriminant analyse (LDA). I denne oppgaven testes nye metoder for (1) preprosessering av MR-bildene, (2) klassifisering av vokslene, og (3) postprosessering basert på klassifiseringsresultatene, for å øke nøyaktigheten til svulstinntegningene. Oppgaven er basert på en studie gjennomført ved Radiumhospitalet i perioden 2001-2004. MR-avbildning av 78 kvinner med lokalavansert livmorhalskreft ble utført i forkant av behandling. Bildene består av T1-vektede, T2-vektede og dynamisk kontrastforsterkede (DCE) bildesekvenser. Samtlige pasienter mottok strålebehandling og kjemoterapi i etterkant. Livmorhalssvulstene ble tegnet inn av to radiologer. Radiologinntegningene ble brukt i modelltreningen og som fasit for å vurdere den automatiske inntegning. For å forbedre analysegrunnlaget og kompensere for variasjoner i MR-bildenes intensitet mellom hver pasient, ble Median filter, Savitzky-Golay filter og Contrast Limited Adaptive Histogram Equalization (CLAHE) testet som preprosesseringsalgoritmer. En postprosesseringsmetode ble implementert, hvor en ROI (Region of Interest) maske tegnes rundt området hvor LDA klassifiseringen predikerte høy sannsynlighet for svulst. Dette fjerner irrelevante områder og reduserer vokselantallet, og det undersøkes om vokselklassifiseringen forbedres. Til slutt ble det undersøkt om ikke-lineære klassifiseringsmetoder skiller svulst og frisk vev mer nøyaktig enn den lineære klassifiseringsmetoden LDA. Klassifiseringsmetodene Random Forest, AdaBoost, k nærmeste nabo (kNN) og støtte vektor maskiner (SVM) ble testet. Resultatene viser at postprosesseringen ved å velge en ROI maske ga en signifikant forbedring av vokselklassifiseringen. Hverken Random Forest, AdaBoost eller kNN klassifiseringsmetodene ga en signifikant forbedring av klassifiseringen. I tillegg brukte disse metodene vesentlig lenger tid på modelltreningen enn LDA. Uttesting av SVM klassifiseringsmetoden ble ikke fullført på grunn av for tidkrevende modelltrening. Ingen av preprosesseringsmetodene ga en forbedring av vokselklassifiseringen. Den beste LDA klassifiseringen med ROI masken ga en gjennomsnittlig DSC og Kappa verdi på henholdsvis 0,53 og 0,51, og sensitivitet og spesifisitet på henholdsvis 0,91 og 0,81. Kappa verdien for LDA modellen var høyere enn forventet overenstemmelsen mellom radiologer, med en multirater Kappa på 0,32. Således viser det diagnostiske verktøyet for automatisk svulstinntegning av livmorhalskreft potensiale til å blir et nyttig verktøy for radiologer.Tumour delineation in medical imaging is a challenging, time-consuming and increasingly complex part of radiotherapy planning. The aim of this thesis is to further develop and improve an automatic tumour delineation program, based on MR-images of patients with cervical cancer. The program classifies voxels in MR-images into two classes, tumour and non-tumour, using linear discriminant analysis (LDA). In this thesis new methods were tested for (1) pre-processing of the MRI images, (2) classification of the voxels as tumour/non-tumour and (3) post-processing of the MRI images based on the classification results. The analysis is based on a study done by the Norwegian Radium Hospital in the period 2001-2004. MRI imaging of 78 women with locally advanced cervical cancer was performed prior to treatment. The MRI images consist of T1-weighted, T2-weighted and dynamic contrast-enhanced (DCE) sequences. All patients received curative radiotherapy with adjuvant chemotherapy afterwards. The tumours were outlined by two radiologists. These contours were used in the modeltraining and as the ground truth to evaluate the autodelineation. To compensate for intensity variations between the patients, the Median Filter, the Savitzky-Golay Filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) were tested as pre-processing algorithms. To improve voxel classification, a post-processing method was implemented, where the results from the initial linear classification were used to draw a mask called Region of Interest (ROI) around the image region predicted to contain the tumour. This mask removed irrelevant areas and reduced the number of voxels. Finally, different classification methods were tested to investigate if non-linear classification models performed better than the linear classification model implemented in the program. The classifiers Random Forest, AdaBoost, k nearest neighbour (kNN) and support vector machines (SVM) were tested. The analysis showed that post-processing by selecting a ROI mask significantly improved voxel classification. Neither Random Forest, AdaBoost nor the kNN classification models gave significantly better classification. In addition, these methods used significantly longer time to train the model, than LDA. The SVM classification method was not sufficiently tested because of time-consuming training. None of the pre-processing methods gave a better voxel classification. The best LDA classification using the ROI post-processed images gave mean DSC and Kappa values of 0.53 and 0.51, respectively, and sensitivity and specificity values of 0.91 and 0.81, respectively. The Kappa values for the LDA model were higher than the expected agreement between radiologists, with a multirater Kappa of 0.32. Subsequently, the autodelineation program for cervical cancers has the potential to become a useful tool for radiologists.M-M

Creating 3D Model of Temporomandibular Joint

Dizertační práce pojednává o 3D rekonstrukci temporomandibulárního kloubu z 2D řezů tkání získané z magnetické rezonance. Současná praxe používá 2D MRI řezů pro určení diagnózy. 3Dmodel má mnoho výhod pro určení diagnózy, které vycházejí ze znalosti prostorové informace. Současná medicína používá 3D modely tkání, ale u tkáně čelistního kloubu existuje problém se segmentací kloubního disku. Tato malá tkáň, která má malý kontrast a velice podobné statistické vlastnosti, jako její okolí, lze jen složitě segmentovat. Pro segmentaci kloubního disku byly vyvinuty nové metody založené na znalosti anatomie oblasti kloubního disku a dále na statistice využívající genetického algoritmu. Soubor 2D řezu má různé rozlišení v osách x,y a ose z. Pro sjednocení rozlišení byl vyvinut algoritmus nadvzorkování, který se snaží zachovat tvarové vlastnosti tkáně. V poslední fázi tvorby 3D modelů bylo využito již standardně používaných metod, avšak tyto metody pro decimaci a vyhlazení mají různé možnosti nastavení (počet polygonů modelu, počet iterací algoritmu). Protože výsledkem práce je získání co nejvěrnějšího modelu reálné tkáně, bylo nutné vytvořit objektivní metody, pomocí kterých by bylo možné nastavit algoritmy tak, aby bylo dosaženo co nejlepšího kompromisu mezi mírou zkreslení a dosažením věrohodnosti modelu.The dissertation thesis deals with 3D reconstruction of the temporomandibular joint from 2D slices of tissue obtained by magnetic resonance. The current practice uses 2D MRI slices in diagnosing. 3D models have many advantages for the diagnosis, which are based on the knowledge of spatial information. Contemporary medicine uses 3D models of tissues, but with the temporomandibular joint tissues there is a problem with segmenting the articular disc. This small tissue, which has a low contrast and very similar statistical characteristics to its neighborhood, is very complicated to segment. For the segmentation of the articular disk new methods were developed based on the knowledge of the anatomy of the joint area of the disk and on the genetic-algorithm-based statistics. A set of 2D slices has different resolutions in the x-, y- and z-axes. An up-sampling algorithm, which seeks to preserve the shape properties of the tissue was developed to unify the resolutions in the axes. In the last phase of creating 3D models standard methods were used, but these methods for smoothing and decimating have different settings (number of polygons in the model, the number of iterations of the algorithm). As the aim of this thesis is to obtain the most precise model possible of the real tissue, it was necessary to establish an objective method by which it would be possible to set the algorithms so as to achieve the best compromise between the distortion and the model credibility achieve.