Machine Learning towards General Medical Image Segmentation

The quality of patient care associated with diagnostic radiology is proportionate to a physician\u27s workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object\u27s contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net\u27s performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency

Evaluering av maskinlæringsmetoder for automatisk tumorsegmentering

The definition of target volumes and organs at risk (OARs) is a critical part of radiotherapy planning. In routine practice, this is typically done manually by clinical experts who contour the structures in medical images prior to dosimetric planning. This is a time-consuming and labor-intensive task. Moreover, manual contouring is inherently a subjective task and substantial contour variability can occur, potentially impacting on radiotherapy treatment and image-derived biomarkers. Automatic segmentation (auto-segmentation) of target volumes and OARs has the potential to save time and resources while reducing contouring variability. Recently, auto-segmentation of OARs using machine learning methods has been integrated into the clinical workflow by several institutions and such tools have been made commercially available by major vendors. The use of machine learning methods for auto-segmentation of target volumes including the gross tumor volume (GTV) is less mature at present but is the focus of extensive ongoing research. The primary aim of this thesis was to investigate the use of machine learning methods for auto-segmentation of the GTV in medical images. Manual GTV contours constituted the ground truth in the analyses. Volumetric overlap and distance-based metrics were used to quantify auto-segmentation performance. Four different image datasets were evaluated. The first dataset, analyzed in papers I–II, consisted of positron emission tomography (PET) and contrast-enhanced computed tomography (ceCT) images of 197 patients with head and neck cancer (HNC). The ceCT images of this dataset were also included in paper IV. Two datasets were analyzed separately in paper III, namely (i) PET, ceCT, and low-dose CT (ldCT) images of 86 patients with anal cancer (AC), and (ii) PET, ceCT, ldCT, and T2 and diffusion-weighted (T2W and DW, respectively) MR images of a subset (n = 36) of the aforementioned AC patients. The last dataset consisted of ceCT images of 36 canine patients with HNC and was analyzed in paper IV. In paper I, three approaches to auto-segmentation of the GTV in patients with HNC were evaluated and compared, namely conventional PET thresholding, classical machine learning algorithms, and deep learning using a 2-dimensional (2D) U-Net convolutional neural network (CNN). For the latter two approaches the effect of imaging modality on auto-segmentation performance was also assessed. Deep learning based on multimodality PET/ceCT image input resulted in superior agreement with the manual ground truth contours, as quantified by geometric overlap and distance-based performance evaluation metrics calculated on a per patient basis. Moreover, only deep learning provided adequate performance for segmentation based solely on ceCT images. For segmentation based on PET-only, all three approaches provided adequate segmentation performance, though deep learning ranked first, followed by classical machine learning, and PET thresholding. In paper II, deep learning-based auto-segmentation of the GTV in patients with HNC using a 2D U-Net architecture was evaluated more thoroughly by introducing new structure-based performance evaluation metrics and including qualitative expert evaluation of the resulting auto-segmentation quality. As in paper I, multimodal PET/ceCT image input provided superior segmentation performance, compared to the single modality CNN models. The structure-based metrics showed quantitatively that the PET signal was vital for the sensitivity of the CNN models, as the superior PET/ceCT-based model identified 86 % of all malignant GTV structures whereas the ceCT-based model only identified 53 % of these structures. Furthermore, the majority of the qualitatively evaluated auto-segmentations (~ 90 %) generated by the best PET/ceCT-based CNN were given a quality score corresponding to substantial clinical value. Based on papers I and II, deep learning with multimodality PET/ceCT image input would be the recommended approach for auto-segmentation of the GTV in human patients with HNC. In paper III, deep learning-based auto-segmentation of the GTV in patients with AC was evaluated for the first time, using a 2D U-Net architecture. Furthermore, an extensive comparison of the impact of different single modality and multimodality combinations of PET, ceCT, ldCT, T2W, and/or DW image input on quantitative auto-segmentation performance was conducted. For both the 86-patient and 36-patient datasets, the models based on PET/ceCT provided the highest mean overlap with the manual ground truth contours. For this task, however, comparable auto-segmentation quality was obtained for solely ceCT-based CNN models. The CNN model based solely on T2W images also obtained acceptable auto-segmentation performance and was ranked as the second-best single modality model for the 36-patient dataset. These results indicate that deep learning could prove a versatile future tool for auto-segmentation of the GTV in patients with AC. Paper IV investigated for the first time the applicability of deep learning-based auto-segmentation of the GTV in canine patients with HNC, using a 3-dimensional (3D) U-Net architecture and ceCT image input. A transfer learning approach where CNN models were pre-trained on the human HNC data and subsequently fine-tuned on canine data was compared to training models from scratch on canine data. These two approaches resulted in similar auto-segmentation performances, which on average was comparable to the overlap metrics obtained for ceCT-based auto-segmentation in human HNC patients. Auto-segmentation in canine HNC patients appeared particularly promising for nasal cavity tumors, as the average overlap with manual contours was 25 % higher for this subgroup, compared to the average for all included tumor sites. In conclusion, deep learning with CNNs provided high-quality GTV autosegmentations for all datasets included in this thesis. In all cases, the best-performing deep learning models resulted in an average overlap with manual contours which was comparable to the reported interobserver agreements between human experts performing manual GTV contouring for the given cancer type and imaging modality. Based on these findings, further investigation of deep learning-based auto-segmentation of the GTV in the given diagnoses would be highly warranted.Definisjon av målvolum og risikoorganer er en kritisk del av planleggingen av strålebehandling. I praksis gjøres dette vanligvis manuelt av kliniske eksperter som tegner inn strukturenes konturer i medisinske bilder før dosimetrisk planlegging. Dette er en tids- og arbeidskrevende oppgave. Manuell inntegning er også subjektiv, og betydelig variasjon i inntegnede konturer kan forekomme. Slik variasjon kan potensielt påvirke strålebehandlingen og bildebaserte biomarkører. Automatisk segmentering (auto-segmentering) av målvolum og risikoorganer kan potensielt spare tid og ressurser samtidig som konturvariasjonen reduseres. Autosegmentering av risikoorganer ved hjelp av maskinlæringsmetoder har nylig blitt implementert som del av den kliniske arbeidsflyten ved flere helseinstitusjoner, og slike verktøy er kommersielt tilgjengelige hos store leverandører av medisinsk teknologi. Auto-segmentering av målvolum inkludert tumorvolumet gross tumor volume (GTV) ved hjelp av maskinlæringsmetoder er per i dag mindre teknologisk modent, men dette området er fokus for omfattende pågående forskning. Hovedmålet med denne avhandlingen var å undersøke bruken av maskinlæringsmetoder for auto-segmentering av GTV i medisinske bilder. Manuelle GTVinntegninger utgjorde grunnsannheten (the ground truth) i analysene. Mål på volumetrisk overlapp og avstand mellom sanne og predikerte konturer ble brukt til å kvantifisere kvaliteten til de automatisk genererte GTV-konturene. Fire forskjellige bildedatasett ble evaluert. Det første datasettet, analysert i artikkel I–II, bestod av positronemisjonstomografi (PET) og kontrastforsterkede computertomografi (ceCT) bilder av 197 pasienter med hode/halskreft. ceCT-bildene i dette datasettet ble også inkludert i artikkel IV. To datasett ble analysert separat i artikkel III, nemlig (i) PET, ceCT og lavdose CT (ldCT) bilder av 86 pasienter med analkreft, og (ii) PET, ceCT, ldCT og T2- og diffusjonsvektet (henholdsvis T2W og DW) MR-bilder av en undergruppe (n = 36) av de ovennevnte analkreftpasientene. Det siste datasettet, som bestod av ceCT-bilder av 36 hunder med hode/halskreft, ble analysert i artikkel IV

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

Methods for the integration of combined PET/MR into radiotherapy planning

Despite recent advances in radiotherapy (RT) there are still tumor types for which a high fraction of recurrences is observed following treatment. Limiting factors in current treatment concepts seem to be inaccuracies in image-based tumor delineation and missing consideration of the biological heterogeneity of individual tumors. In this respect, the abundant anatomical and functional information provided by magnetic resonance imaging (MRI) and positron emission tomography (PET) may lead to major advances in RT treatment. Recently available combined PET/MR scanners allow for the acquisition of simultaneous, intrinsically registered PET/MR data, facilitating their combined analysis for the integration into RT. In this thesis, dedicated methods and algorithms for the analysis and integration of the multimodal PET/MR datasets into RT are developed. In the first part, a method for multimodal deformable registration is developed, to enable the spatial transformation of PET/MR data to the computed tomography used for treatment planning. The second part is concerned with the development of an automatic tumor segmentation algorithm, considering PET and MR information simultaneously. In the last part, a correlation analysis of various functional datasets is motivated and performed in order to support the definition of a biologically adapted dose prescription.Trotz jüngster Fortschritte in der Strahlentherapie (ST) gibt es noch immer Tumorarten mit einem hohen Prozentsatz an Rezidiven nach der Behandlung. Limitierende Faktoren in aktuellen Behandlungskonzepten scheinen vor allem Ungenauigkeiten in der bildbasierten Tumorabgrenzung sowie die fehlende Berücksichtigung der biologischen Heterogenität der einzelnen Tumoren zu sein. In dieser Hinsicht erscheint die Einbeziehung der vielfältigen anatomischen und funktionellen Bildgebungsmöglichkeiten der Magnetresonanztomographie (MRT), sowie der Positronenemissionstomographie (PET), in die ST vielversprechend. Seit kurzem verfügbare PET/MR-Scanner erlauben die Akquisition simultaner, intrinsisch registrierter PET/MR-Datensätze, wodurch deren kombinierte Analyse und Integration in die Therapieplanung erleichtert wird. Diese Arbeit befasst sich mit der Entwicklung von dedizierten Methoden und Algorithmen für die Analyse und Integration der multimodalen PET/MR-Datensätze in die ST. Im ersten Teilprojekt wurde eine Methode zur multimodalen deformierbaren Registrierung entwickelt, um die räumliche Transformation der PET/MR-Daten auf die zur Therapieplanung notwendige Computertomographie-Aufnahme zu ermöglichen. Im zweiten Teil wurde ein Algorithmus zur automatischen Tumorsegmentierung unter simultaner Berücksichtigung von PET- und MR-Information entwickelt. Abschließend wurde im dritten Teil eine Korrelationsanalyse der funktionellen PET- und MR-Datensätze motiviert und ausgeführt, um die Definition einer biologisch adaptierten Dosisverschreibung zu unterstützen

Quantitative analysis with machine learning models for multi-parametric brain imaging data

Gliomas are considered to be the most common primary adult malignant brain tumor. With the dramatic increases in computational power and improvements in image analysis algorithms, computer-aided medical image analysis has been introduced into clinical applications. Precision tumor grading and genotyping play an indispensable role in clinical diagnosis, treatment and prognosis. Gliomas diagnostic procedures include histopathological imaging tests, molecular imaging scans and tumor grading. Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human study has limitations that can result in low reproducibility and inter-observer agreement. Compared with histopathological images, Magnetic resonance (MR) imaging present the different structure and functional features, which might serve as noninvasive surrogates for tumor genotypes. Therefore, computer-aided image analysis has been adopted in clinical application, which might partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure multilevel features on multi-parametric medical information. Imaging features obtained from a single modal image do not fully represent the disease, so quantitative imaging features, including morphological, structural, cellular and molecular level features, derived from multi-modality medical images should be integrated into computer-aided medical image analysis. The image quality differentiation between multi-modality images is a challenge in the field of computer-aided medical image analysis. In this thesis, we aim to integrate the quantitative imaging data obtained from multiple modalities into mathematical models of tumor prediction response to achieve additional insights into practical predictive value. Our major contributions in this thesis are: 1. Firstly, to resolve the imaging quality difference and observer-dependent in histological image diagnosis, we proposed an automated machine-learning brain tumor-grading platform to investigate contributions of multi-parameters from multimodal data including imaging parameters or features from Whole Slide Images (WSI) and the proliferation marker KI-67. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. A quantitative interpretable machine learning approach (Local Interpretable Model-Agnostic Explanations) was followed to measure the contribution of features for single case. Most grading systems based on machine learning models are considered “black boxes,” whereas with this system the clinically trusted reasoning could be revealed. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. 2. Based on the automated brain tumor-grading platform we propose, multimodal Magnetic Resonance Images (MRIs) have been introduced in our research. A new imaging–tissue correlation based approach called RA-PA-Thomics was proposed to predict the IDH genotype. Inspired by the concept of image fusion, we integrate multimodal MRIs and the scans of histopathological images for indirect, fast, and cost saving IDH genotyping. The proposed model has been verified by multiple evaluation criteria for the integrated data set and compared to the results in the prior art. The experimental data set includes public data sets and image information from two hospitals. Experimental results indicate that the model provided improves the accuracy of glioma grading and genotyping

Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy

Objective: Adaptive radiotherapy workflows require images with the quality of computed tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve quality of cone beam CT (CBCT) images for dose calculation using deep learning. / Approach: We propose a novel framework for CBCT-to-CT synthesis using cycle-consistent Generative 10 Adversarial Networks (cycleGANs). The framework was tailored for paediatric abdominal patients, a challenging application due to the inter-fractional variability in bowel filling and smaller patient numbers. We introduced the concept of global residuals only learning to the networks and modified the cycleGAN loss function to explicitly promote structural consistency between source and synthetic images. Finally, to compensate for the anatomical variability and address the difficulties in collecting large datasets in the 15 paediatric population, we applied a smart 2D slice selection based on the common field-of-view across the dataset (abdomen). This acted as a weakly paired data approach that allowed us to take advantage of scans from patients treated for a variety of malignancies (thoracic-abdominal-pelvic) for training purposes. We first optimised the proposed framework and benchmarked its performance on a development dataset. Later, a comprehensive quantitative evaluation was performed on an unseen 20 dataset, which included calculating global image similarity metrics, segmentation-based measures and proton therapy-specific metrics. / Main results: We found improved performance, compared to a baseline implementation, on imagesimilarity metrics such as Mean Absolute Error calculated for a matched virtual CT (55.0±16.6 proposed vs 58.9±16.8 baseline). There was also a higher level of structural agreement for gastrointestinal gas 25 between source and synthetic images measured through dice similarity overlap (0.872±0.053 proposed vs 0.846±0.052 baseline). Differences found in water-equivalent thickness metrics were also smaller for our method (3.3±2.4% proposed vs 3.7±2.8% baseline). / Significance: Our findings indicate that our innovations to the cycleGAN framework improved the quality and structure consistency of the synthetic CTs generated