Computer aided assessment of CT scans of traumatic brain injury patients

A thesis submitted in partial fulfilment for the degree of Doctor of PhilosophyOne of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues

Automated Characterisation and Classification of Liver Lesions From CT Scans

Cancer is a general term for a wide range of diseases that can affect any part of the body due to the rapid creation of abnormal cells that grow outside their normal boundaries. Liver cancer is one of the common diseases that cause the death of more than 600,000 each year. Early detection is important to diagnose and reduce the incidence of death. Examination of liver lesions is performed with various medical imaging modalities such as Ultrasound (US), Computer tomography (CT), and Magnetic resonance imaging (MRI). The improvements in medical imaging and image processing techniques have significantly enhanced the interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate. Moreover, CAD systems can help physician, as a second opinion, in characterising lesions and making the diagnostic decision. Thus, CAD systems have become an important research area. Particularly, these systems can provide diagnostic assistance to doctors to improve overall diagnostic accuracy. The traditional methods to characterise liver lesions and differentiate normal liver tissues from abnormal ones are largely dependent on the radiologists experience. Thus, CAD systems based on the image processing and artificial intelligence techniques gained a lot of attention, since they could provide constructive diagnosis suggestions to clinicians for decision making. The liver lesions are characterised through two ways: (1) Using a content-based image retrieval (CBIR) approach to assist the radiologist in liver lesions characterisation. (2) Calculating the high-level features that describe/ characterise the liver lesion in a way that is interpreted by humans, particularly Radiologists/Clinicians, based on the hand-crafted/engineered computational features (low-level features) and learning process. However, the research gap is related to the high-level understanding and interpretation of the medical image contents from the low-level pixel analysis, based on mathematical processing and artificial intelligence methods. In our work, the research gap is bridged if a relation of image contents to medical meaning in analogy to radiologist understanding is established. This thesis explores an automated system for the classification and characterisation of liver lesions in CT scans. Firstly, the liver is segmented automatically by using anatomic medical knowledge, histogram-based adaptive threshold and morphological operations. The lesions and vessels are then extracted from the segmented liver by applying AFCM and Gaussian mixture model through a region growing process respectively. Secondly, the proposed framework categorises the high-level features into two groups; the first group is the high-level features that are extracted from the image contents such as (Lesion location, Lesion focality, Calcified, Scar, ...); the second group is the high-level features that are inferred from the low-level features through machine learning process to characterise the lesion such as (Lesion density, Lesion rim, Lesion composition, Lesion shape,...). The novel Multiple ROIs selection approach is proposed, in which regions are derived from generating abnormality level map based on intensity difference and the proximity distance for each voxel with respect to the normal liver tissue. Then, the association between low-level, high-level features and the appropriate ROI are derived by assigning the ability of each ROI to represents a set of lesion characteristics. Finally, a novel feature vector is built, based on high-level features, and fed into SVM for lesion classification. In contrast with most existing research, which uses low-level features only, the use of high-level features and characterisation helps in interpreting and explaining the diagnostic decision. The methods are evaluated on a dataset containing 174 CT scans. The experimental results demonstrated that the efficacy of the proposed framework in the successful characterisation and classification of the liver lesions in CT scans. The achieved average accuracy was 95:56% for liver lesion characterisation. While the lesion’s classification accuracy was 97:1% for the entire dataset. The proposed framework is developed to provide a more robust and efficient lesion characterisation framework through comprehensions of the low-level features to generate semantic features. The use of high-level features (characterisation) helps in better interpretation of CT liver images. In addition, the difference-of-features using multiple ROIs were developed for robust capturing of lesion characteristics in a reliable way. This is in contrast to the current research trend of extracting the features from the lesion only and not paying much attention to the relation between lesion and surrounding area. The design of the liver lesion characterisation framework is based on the prior knowledge of the medical background to get a better and clear understanding of the liver lesion characteristics in medical CT images

The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning

Diagnostic Performance of Deep Learning-Based Lesion Detection Algorithm in CT for Detecting Hepatic Metastasis from Colorectal Cancer

Objective: To compare the performance of the deep learning-based lesion detection algorithm (DLLD) in detecting liver metastasis with that of radiologists. Materials and methods: This clinical retrospective study used 4386-slice computed tomography (CT) images and labels from a training cohort (502 patients with colorectal cancer [CRC] from November 2005 to December 2010) to train the DLLD for detecting liver metastasis, and used CT images of a validation cohort (40 patients with 99 liver metastatic lesions and 45 patients without liver metastasis from January 2011 to December 2011) for comparing the performance of the DLLD with that of readers (three abdominal radiologists and three radiology residents). For per-lesion binary classification, the sensitivity and false positives per patient were measured. Results: A total of 85 patients with CRC were included in the validation cohort. In the comparison based on per-lesion binary classification, the sensitivity of DLLD (81.82%, [81/99]) was comparable to that of abdominal radiologists (80.81%, p = 0.80) and radiology residents (79.46%, p = 0.57). However, the false positives per patient with DLLD (1.330) was higher than that of abdominal radiologists (0.357, p < 0.001) and radiology residents (0.667, p < 0.001). Conclusion: DLLD showed a sensitivity comparable to that of radiologists when detecting liver metastasis in patients initially diagnosed with CRC. However, the false positives of DLLD were higher than those of radiologists. Therefore, DLLD could serve as an assistant tool for detecting liver metastasis instead of a standalone diagnostic tool.ope