METHODS: In this work, we introduce a fully automated liver tumour segmentation approach in contrast-enhanced CT datasets. The method is a multi-stage technique which starts with contrast enhancement of the tumours using anisotropic filtering, followed by adaptive thresholding to extract the initial mask of the tumours from an identified liver region of interest. Localised level set-based active contours are used to extend the mask to the tumour boundaries.
RESULTS: The proposed method is validated on the IRCAD database with pathologies that offer highly variable and complex liver tumours. The results are compared quantitatively to the ground truth, which is delineated by experts. We achieved an average dice similarity coefficient of 75% over all patients with liver tumours in the database with overall absolute relative volume difference of 11%. This is comparable to other recent works, which include semiautomated methods, although they were validated on different datasets.
CONCLUSIONS: The proposed approach aims to segment tumours inside the liver envelope automatically with a level of accuracy adequate for its use as a tool for surgical planning using abdominal CT images. The approach will be validated on larger datasets in the future.
OBJECTIVE: The aim of this study is to evaluate the ICV in primary craniosynostosis patients after the cranial vault reshaping with or without FOA and to compare between syndromic and nonsyndromic synostosis group, to determine factors that associated with significant changes in the ICV postoperative, and to evaluate the resolution of copper beaten sign and improvement in neurodevelopmental delay after the surgery.
METHODS: This is a prospective observational study of all primary craniosynostosis patients who underwent operation cranial vault reshaping with or without FOA in Hospital Kuala Lumpur from January 2017 until Jun 2018. The ICV preoperative and postoperative was measured using the 3D computed tomography (CT) imaging and analyzed. The demographic data, clinical and radiological findings were identified and analyzed.
RESULTS: A total of 14 cases (6 males and 8 females) with 28 3D CT scans were identified. The mean age of patients was 23 months. Seven patients were having syndromic synostosis (4 Crouzon syndromes and 3 Apert syndromes) and 7 nonsyndromic synostosis. The mean preoperative ICV was 880 mL (range, 641-1234 mL), whereas the mean postoperative ICV was 1081 mL (range, 811-1385 mL). The difference was 201 mL which was statistically significant (P 1.0). However, there was 100% (n = 13) improvement of this copper beaten sign. However, the neurodevelopmental delay showed no improvement which was statistically not significant (P > 1.0).
CONCLUSION: Surgery in craniosynostosis patient increases the ICV besides it improves the shape of the head. From this study, the syndromic synostosis had better increment of ICV compared to nonsyndromic synostosis.
METHODS: This is a retrospective observational study where 25 male in-patients with laboratory-confirmed COVID-19 in Hospital Canselor Tuanku Muhriz. Demographics, clinical data and CT images of these patients were reviewed by 2 senior radiologists.
RESULTS: In total there were 25 patients (all males; mean age [±SD], 21.64±2.40 years; range, 18-27 years). Patients with abnormal chest CT showed a relatively low normal absolute lymphocytes count (median: 2.2 x 109/L) and absolute monocyte count (median: 0.5 x 109/L). Lactate dehydrogenase was elevated in 5 (20%) of the patients. The procalcitonin level was normal while elevated levels of alanine aminotransferase, total bilirubin, platelet and C-reactive protein were common. Baseline chest CT showed abnormalities in 6 patients. The distribution of the lesions were; upper lobe 3 (12%) lower lobe 3 (12%) with peripheral distribution 4 (16%). Of the 25 patients included, 4 (16%) had ground glass opacification (GGO), 1 (4%) had a small peripheral subpleural nodule, and 1 (4%) had a dense solitary granuloma. Four patients had typical CT features of COVID-19.
CONCLUSION: We found that the CT imaging showed peripheral GGO in our patients. They remained clinically stable with no deterioration of their respiratory symptoms suggesting stability in lung involvement. We postulate that rapid changes in CT imaging may not be present in young, asymptomatic, non-smoking COVID-19 patients. Thus the use of CT thoraxfor early diagnosis may be reserved for patients in the older agegroups, and not in younger patients.
METHODS: We propose to use Residual Blocks with a 3 × 3 kernel size for local feature extraction and Non-Local Blocks to extract the global features. The Non-Local Block has the ability to extract global features without using a huge number of parameters. The key idea behind the Non-Local Block is to apply matrix multiplications between features on the same feature maps.
RESULTS: We trained and validated the proposed method on the LIDC-IDRI dataset which contains 1018 computed tomography scans. We followed a rigorous procedure for experimental setup, namely tenfold cross-validation, and ignored the nodules that had been annotated by
METHODS: In this study, we propose a multi-view secondary input residual (MV-SIR) convolutional neural network model for 3D lung nodule segmentation using the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset of chest computed tomography (CT) images. Lung nodule cubes are prepared from the sample CT images. Further, from the axial, coronal, and sagittal perspectives, multi-view patches are generated with randomly selected voxels in the lung nodule cubes as centers. Our model consists of six submodels, which enable learning of 3D lung nodules sliced into three views of features; each submodel extracts voxel heterogeneity and shape heterogeneity features. We convert the segmentation of 3D lung nodules into voxel classification by inputting the multi-view patches into the model and determine whether the voxel points belong to the nodule. The structure of the secondary input residual submodel comprises a residual block followed by a secondary input module. We integrate the six submodels to classify whether voxel points belong to nodules, and then reconstruct the segmentation image.
RESULTS: The results of tests conducted using our model and comparison with other existing CNN models indicate that the MV-SIR model achieves excellent results in the 3D segmentation of pulmonary nodules, with a Dice coefficient of 0.926 and an average surface distance of 0.072.
CONCLUSION: our MV-SIR model can accurately perform 3D segmentation of lung nodules with the same segmentation accuracy as the U-net model.
METHODS: We sourced articles from Scopus, Ovid and PubMed databases for journal publications related to post-mortem diagnostic imaging. We highlight the most relevant full articles in English that explain the type of modality that was utilised and the added value it provided for diagnosing the cause of death.
RESULTS: Minimally invasive autopsies assisted by imaging modalities added a great benefit to forensic medicine, and supported conventional autopsy. In particular the role of post mortem computed tomography (PMCT), post mortem computed tomography angiography (PMMR) and positron emission tomography computed tomography (PMCTA) that have incremental benefits in diagnosing traumatic death, fractures, tissue injuries, as well as the assessment of body height or weight for corpse identification.
CONCLUSION: PMCT and PMMR, with particular emphasis on PMCTA, can provide higher accuracy than the other modalities. They can be regarded as indispensable methods that should be applied to the routine autopsy protocol, thus improving the findings and accuracy of diagnosing the cause of death.