Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134994/1/mp2679.pd

Validation of an elastic registration technique to estimate anatomical lung modification in Non-Small-Cell Lung Cancer Tomotherapy

<p>Abstract</p> <p>Background</p> <p>The study of lung parenchyma anatomical modification is useful to estimate dose discrepancies during the radiation treatment of Non-Small-Cell Lung Cancer (NSCLC) patients. We propose and validate a method, based on free-form deformation and mutual information, to elastically register planning kVCT with daily MVCT images, to estimate lung parenchyma modification during Tomotherapy.</p> <p>Methods</p> <p>We analyzed 15 registrations between the planning kVCT and 3 MVCT images for each of the 5 NSCLC patients. Image registration accuracy was evaluated by visual inspection and, quantitatively, by Correlation Coefficients (CC) and Target Registration Errors (TRE). Finally, a lung volume correspondence analysis was performed to specifically evaluate registration accuracy in lungs.</p> <p>Results</p> <p>Results showed that elastic registration was always satisfactory, both qualitatively and quantitatively: TRE after elastic registration (average value of 3.6 mm) remained comparable and often smaller than voxel resolution. Lung volume variations were well estimated by elastic registration (average volume and centroid errors of 1.78% and 0.87 mm, respectively).</p> <p>Conclusions</p> <p>Our results demonstrate that this method is able to estimate lung deformations in thorax MVCT, with an accuracy within 3.6 mm comparable or smaller than the voxel dimension of the kVCT and MVCT images. It could be used to estimate lung parenchyma dose variations in thoracic Tomotherapy.</p

Deep learning for lung cancer analysis

This thesis describes the development and evaluation of two novel deep learning applications that tackle two cancers that affect the lungs. The first, lung cancer, is the largest cause of cancer-related deaths in the United Kingdom. It accounts for more than 1 in 5 cancer deaths; around 35,000 people every year. Lung cancer is curable providing it is detected very early. Computed tomography (CT) X-ray imaging has been shown to be effective for screening. However, the resulting 3D medical images are laborious for humans to read, and widespread adoption would put a huge strain on already stretched radiology departments. I developed a novel deep learning based approach for the automatic detection of lung nodules, potential early lung cancer, that has potential to reduce human workloads. It was evaluated on two independent datasets, and achieves performance competitive with published state-of-the-art tools, with average sensitivity of 84% to 92% at 8 false positives per scan. I developed a related invention which allows hierarchical relationships to be leveraged to improve the performance of CAD tools like this for detection and segmentation tasks. The second cancer is malignant pleural mesothelioma. It is very different from lung cancer: rather than growing within the lung, mesothelioma grows around the outside of the lung in the pleural cavity, like the rind on an orange. It is a rare cancer, caused by exposure to asbestos fibres. It can take decades from exposure to symptoms developing. In Glasgow many mesothelioma patients worked in the ship-building industry, which relied heavily on asbestos. Although asbestos has been banned in the UK since 1999, its presence in buildings and equipment built before then mean that mesothelioma will remain a problem for years to come. Sadly, asbestos is still being mined and many countries, including the United States, have still not instigated a complete ban. For mesothelioma the main challenge is not detection, but accurate measurement —- without the ability to measure tumour size it is difficult to evaluate potential treatments. We therefore developed a fully automated volumetric assessment of malignant pleural mesothelioma. Performance of the algorithm is shown on a multi-centre test set, where volumetric predictions are highly correlated with an expert annotator (r=0.851, p<0.0001). Region overlap scores between the automated method and an expert annotator exceed those for inter-annotator agreement across a subset of cases. Dice overlap scores of 0.64 and 0.55, by cross-validation and independent testing respectively, were achieved. Future work will progress this algorithm towards clinical deployment for the automated assessment of longitudinal tumour development

Role of static fluid MR urography in detecting post urinary diversion complications

Aim of work: The aim of the study was to assess the diagnostic performance of static MR urography in detection of post cystectomy complications &amp; the ability of static fluid MR urography in visualization of urinary tract segments.Material &amp; methods: We prospectively reviewed 21 MR urograms with urinary diversion. The most common surgical procedures included Ileal conduit &amp; Ileocecal neobladder diversion.Material &amp; methods: Magnetic resonance urography examinations were performed with 1.5-T MR scanners. T2 weighted (static fluid) MR urography techniques were done, in addition to conventional T1- and T2-weighted axial and coronal sequences. Urinary tract was divided in different parts: pelvicalyceal systems, upper, mid and lower ureteric segments &amp; the reservoir or conduit Imaging features of the urinary collecting systems were evaluated for their visualization and complications detection.Results: T2-weighted MR urography could demonstrate 95% of urinary tract segments &amp; together with conventional MR sequences all urinary tract segments can be visualized. Urinary diversion related complications were encountered included in 15 patients (71.4%) &amp; no urological complications were seen in 6 patients (28.6%).Conclusion: Comprehensive T2-weighted MR urography is an effective imaging method for the visualization of the urinary system and detection of early and late postoperative complications in patients with urinary diversion.Keywords: MR urography, Urinary diversion, Cancer bladde

Fully Automatic Danger Zone Determination for SBRT in NSCLC

Lung cancer is the major cause of cancer death worldwide. The most common form of lung cancer is non-small cell lung cancer(NSCLC). Stereotactic body radiation therapy (SBRT) has emerged as a good alternative to surgery in patients with peripheralstage I NSCLC, demonstrating favorable tumor control and low toxicity. Due to spatial relationship to several critical organs atrisk, SBRT of centrally located lesions is associated with more severe toxicity and requires modification in dose application andfractionation, which is currently evaluated in clinical trials. Therefore a classification of lung tumors into central or peripheralis required. In this work we present a novel, highly versatile, mulitmodality tool for tumor classification which requires no userinteraction. Furthermore the tool can automatically segment the trachea, proximal bronchial tree, mediastinum, gross target volumeand internal target volume. The proposed work is evaluated on 19 cases with different image modalities assessing segmentationquality as well as classification accuracy. Experiments showed a good segmentation quality and a classification accuracy of 95 %.These results suggest the use of the proposed tool for clinical trials to assist clinicians in their work and to fasten up the workflowin NSCLC patients treatment

Prediction of Ablation Volume in Percutaneous Lung Microwave Ablation: A Single Centre Retrospective Study

Background: Percutaneous Microwave Ablation (MWA) of lung malignancies is a procedure with many technical challenges, among them the risk of residual disease. Recently, dedicated software able to predict the volume of the ablated area was introduced. Cone-beam computed tomography (CBCT) is the imaging guidance of choice for pulmonary ablation in our institution. The volumetric prediction software (VPS) has been installed and used in combination with CBCT to check the correct position of the device. Our study aimed to compare the results of MWA of pulmonary tumours performed using CBCT with and without VPS. Methods: We retrospectively reviewed 1-month follow-up enhanced contrast-enhanced computed tomography (CECT) scans of 10 patients who underwent ablation with the assistance of VPS (group 1) and of 10 patients who were treated without the assistance of VPS (group 2). All patients were treated for curative purposes, the maximum axial diameter of lesions ranged between 5 and 22 mm in group 1 and between 5 and 25 mm in group 2. We compared the presence of residual disease between the two groups. Results: In group 1 residual disease was seen in only 1 patient (10%) in which VPS had ensured complete coverage of the tumour. In group 2 residual disease was found in 3 patients (30%). Conclusions: Using this software during MWA of lung malignancies could improve the efficacy of the treatment compared to the conventional only CBCT guidance

A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs

This paper presents a new hybrid biomechanical model-based non-rigid image registration method for lung motion estimation. In the proposed method, a patient-specific biomechanical modelling process captures major physically realistic deformations with explicit physical modelling of sliding motion, whilst a subsequent non-rigid image registration process compensates for small residuals. The proposed algorithm was evaluated with 10 4D CT datasets of lung cancer patients. The target registration error (TRE), defined as the Euclidean distance of landmark pairs, was significantly lower with the proposed method (TRE = 1.37 mm) than with biomechanical modelling (TRE = 3.81 mm) and intensity-based image registration without specific considerations for sliding motion (TRE = 4.57 mm). The proposed method achieved a comparable accuracy as several recently developed intensity-based registration algorithms with sliding handling on the same datasets. A detailed comparison on the distributions of TREs with three non-rigid intensity-based algorithms showed that the proposed method performed especially well on estimating the displacement field of lung surface regions (mean TRE = 1.33 mm, maximum TRE = 5.3 mm). The effects of biomechanical model parameters (such as Poisson’s ratio, friction and tissue heterogeneity) on displacement estimation were investigated. The potential of the algorithm in optimising biomechanical models of lungs through analysing the pattern of displacement compensation from the image registration process has also been demonstrated

Diagnostic and prognostic biomarkers of malignant pleural mesothelioma

Malignant Pleural Mesothelioma (MPM) is an aggressive intrathoracic malignancy with an overall poor prognosis. MPM is associated with asbestos exposure but has a long latency period between exposure and disease development. Incidence of MPM in the UK is therefore still rising, predicted to reach a peak in 2020. The majority of patients with MPM present with breathlessness, frequently due to a pleural effusion and/or chest pain. Diagnosis of MPM can be difficult. Radiological detection of early stage MPM in particular can be challenging, as pleural tumour, nodularity or significant pleural thickening may not be evident. Diagnosis is further complicated by the low yield of pleural fluid cytology examination in MPM and pleural biopsy is therefore usually required to allow definitive diagnosis. This can be achieved under image guidance, at surgical thoracoscopy or at local anaesthetic thoracoscopy (LAT). A significant number of patients are either elderly or have co-morbidity precluding general anaesthesia and surgical thoracoscopy. Image-guided pleural biopsy is not always feasible, particularly in the absence of significant pleural thickening. LAT remains a limited resource in the UK. A non-invasive biomarker of MPM, which could be performed early in the patient’s presentation, and that could be available to most hospitals, would therefore be a major clinical advance, allowing clinicians to direct appropriate patients to specialist centres with access to LAT and specialist MDT input where MPM appears likely. There have been several potential blood biomarkers identified in the mesothelioma literature, including the most widely studied, Mesothelin, and more recently Fibulin-3 and SOMAscan™. Unfortunately study results have been variably limited by retrospective study design, inconsistent sampling time points, inconsistent results and lack of external validation, therefore despite initial promising results, none of these biomarkers have entered routine clinical practice for diagnosis. Similarly, utility of imaging biomarkers such as perfusion Computed Tomography (CT), Positron Emission Tomography (PET) and Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been limited by high radiation dose, limited availability, and requirement for bulky (and therefore late stage) disease for assessment respectively. In chapter 2, study design, recruitment and preliminary results of the DIAPHRAGM (Diagnostic and Prognostic Biomarkers in the Rational Assessment of Mesothelioma) study are reported. A prospective, multi-centre study was designed, recruiting patients with suspected pleural malignancy (SPM) at initial presentation to secondary care services, from a mixture of academic and more clinical units in the UK and Ireland, in addition to asbestos-exposed control subjects. In one of the largest biomarker studies in mesothelioma to date, 639 patients with SPM and 113 asbestos-exposed control subjects were recruited over three years. Data cleaning is being finalised by the Cancer Research UK Clinical Trials Unit Glasgow at the time of writing. Preliminary results reveal that 26% (n=154) patients recruited to the SPM cohort were diagnosed with MPM, 33% (n=209) had secondary pleural malignancy and 34% (n=218) were diagnosed with benign pleural disease. A final diagnosis is awaited in 7% (n=47) at the time of writing. SOMAscan™ and Fibulin-3 biomarker analyses are ongoing and DIAPHRAGM will definitively answer the question of diagnostic utility of these blood biomarkers in routine clinical practice, in a ‘real-life’ MPM population, relative to that of Mesothelin. In chapter 3, contrast-enhanced MRI was performed in patients with suspected MPM and a novel MRI biomarker of pleural malignancy defined (Early Contrast Enhancement – ECE). ECE was defined as a peak in pleural signal intensity at or before 4.5 minutes after intravenous Gadobutrol administration. ECE assessment was successfully performed in all patients who underwent contrast-enhanced MRI. This included patients with pleural thickening 0.533AU/s), indicative of high tumour vascularity, was associated with poor median overall survival (12 months vs. 20 months, p=0.047). Staging of MPM represents an additional challenge to clinicians. This is due to the complex morphology and often rind-like growth pattern of MPM. In addition, delineation of pleural disease from adjacent structures such as intercostal muscle and diaphragm can be difficult to assess, particularly at CT, which is the most commonly used imaging modality for diagnostic and staging assessment in MPM. Current clinical staging frequently underestimates extent of disease, with a significant proportion of patients being upstaged at time of surgery, and is limited by high inter-observer variability. Recent studies have reported the prognostic significance of CT-derived tumour volume; however, many of these studies have been limited by the laborious or complex nature of tumour segmentation, significant inter-observer variability or challenges encountered in separating pleural tumour from adjacent structures, which are often of similar density. MRI is superior to CT in the detection of invasion of the chest wall and diaphragm in MPM. In Chapter 4, MRI was used to quantitatively assess pleural tumour volume in 31 patients with MPM using novel semi-automated segmentation methodology. Four different segmentation methodologies, using Myrian® segmentation software were developed and examined. Optimum methodology was defined, based on the accuracy of volume estimates of an MRI phantom, visual-based analysis, intra-observer agreement and analysis time. Using the optimum methodology, there was acceptable error around the MRI phantom volume (3.6%), a reasonable analysis time (approximately 14 minutes), good intra-observer agreement (intra-class correlation coefficient (ICC) 0.875) and excellent inter-observer agreement (ICC 0.962). Patients with a high MRI-estimated tumour volume (≥300cm3) had a significantly poorer median overall survival (8.5 months vs. 20 months) and was a statistically significant prognostic variable on univariate (HR 2.273 (95% CI 1.162 – 4.446), p=0.016) and multi-variate Cox proportional hazards model (HR 2.114 (95% CI 1.046 – 4.270), p=0.037)