17 research outputs found

    Deep Learning Framework for Spleen Volume Estimation from 2D Cross-sectional Views

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    Abnormal spleen enlargement (splenomegaly) is regarded as a clinical indicator for a range of conditions, including liver disease, cancer and blood diseases. While spleen length measured from ultrasound images is a commonly used surrogate for spleen size, spleen volume remains the gold standard metric for assessing splenomegaly and the severity of related clinical conditions. Computed tomography is the main imaging modality for measuring spleen volume, but it is less accessible in areas where there is a high prevalence of splenomegaly (e.g., the Global South). Our objective was to enable automated spleen volume measurement from 2D cross-sectional segmentations, which can be obtained from ultrasound imaging. In this study, we describe a variational autoencoder-based framework to measure spleen volume from single- or dual-view 2D spleen segmentations. We propose and evaluate three volume estimation methods within this framework. We also demonstrate how 95% confidence intervals of volume estimates can be produced to make our method more clinically useful. Our best model achieved mean relative volume accuracies of 86.62% and 92.58% for single- and dual-view segmentations, respectively, surpassing the performance of the clinical standard approach of linear regression using manual measurements and a comparative deep learning-based 2D-3D reconstruction-based approach. The proposed spleen volume estimation framework can be integrated into standard clinical workflows which currently use 2D ultrasound images to measure spleen length. To the best of our knowledge, this is the first work to achieve direct 3D spleen volume estimation from 2D spleen segmentations.Comment: 22 pages, 7 figure

    Dilemma in Differentiating between Acute Osteomyelitis and Bone Infarction in Children with Sickle Cell Disease:The Role of Ultrasound

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    BACKGROUND: Distinguishing between acute presentations of osteomyelitis (OM) and vaso-occlusive crisis (VOC) bone infarction in children with sickle cell disease (SCD) remains challenging for clinicians, particularly in culture-negative cases. We examined the combined role of ultrasound scan (USS), C - reactive protein and White blood counts (WCC) in aiding early diagnosis in children with SCD presenting acutely with non-specific symptoms such as bone pain, fever or swelling which are common in acute osteomyelitis or VOC. METHODS: We reviewed the records of all children with SCD who were discharged from our department from October 2003 to December 2010 with a diagnosis of osteomyelitis based on clinical features and the results of radiological and laboratory investigations. A case control group with VOC who were investigated for OM were identified over the same period. RESULTS: In the osteomyelitis group, USS finding of periosteal elevation and/or fluid collection was reported in 76% cases with the first scan (day 0-6). Overall 84% were diagnosed with USS (initial +repeat). 16% had negative USS. With VOC group, USS showed no evidence of fluid collection in 53/58 admissions (91%), none of the repeated USS showed any fluid collection. Mean C-reactive protein (CRP), and white cell count (WCC) were significantly higher in the OM. CONCLUSION: The use of Ultrasound in combination with CRP and WCC is a reliable, cost-effective diagnostic tool for differentiating osteomyelitis from VOC bone infarction in SCD. A repeat ultrasound and/or magnetic resonance imaging (MRI) scan may be is necessary to confirm the diagnosis
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