Skeletal defect at mid-Trimester ultrasound scan

Abstract We present an interesting case of a 70-year-old female who presented with haematuria on the suspected cancer pathway. Renal ultrasound showed a vascular renal mass on her right kidney measuring 8x7cm with an unremarkable left kidney. She underwent a laparoscopic radical nephrectomy following confirmation of an 8cm renal mass in the right kidney on the contrasted staging CT scan with a repeat review at the multidisciplinary meeting. Post-operatively a subsequent review of the pre-operative CT and ultrasound scan, showed an incidental large left(contralateral) upper quadrant retroperitoneal fatty mass sized 15x10cm, displacing the stomach and spleen. Further investigation in the form of an MRI Abdomen excluded features of a liposarcoma, resulting in the diagnosis of a large retroperitoneal lipoma. This case highlights the significance of selective attention in imaging interpretation. We believe this to be a prime example of the level of meticulousness required as fat-rich tissues have low attenuation on CT-scans, which can be easily missed out. A cautious multi-clinician interpretation of scans should be performed to avoid missing potentially sinister pathology which would impact patient care dramatically. This case has led to more thorough review of future pre-operative imaging by the operating surgical team.

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An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

Ultrasonic Imaging ◽

10.1177/0161734621998091 ◽

2021 ◽

pp. 016173462199809

Author(s):

Dhurgham Al-karawi ◽

Hisham Al-Assam ◽

Hongbo Du ◽

Ahmad Sayasneh ◽

Chiara Landolfo ◽

...

Keyword(s):

Gabor Filter ◽

Empirical Evaluation ◽

Texture Features ◽

Image Texture ◽

Support Vector ◽

Simple Majority ◽

Ultrasound Scan ◽

Histograms Of Oriented Gradients ◽

The Individual ◽

Ovarian Masses

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k ( k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

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Comparison of two live-animal ultrasound systems for genetic evaluation of carcass traits in Angus cattle

Translational Animal Science ◽

10.1093/tas/txab011 ◽

2021 ◽

Author(s):

C J Duff ◽

J H J van der Werf ◽

P F Parnell ◽

S A Clark

Keyword(s):

Carcass Traits ◽

Genetic Correlations ◽

Subcutaneous Fat ◽

Genetic Evaluation ◽

Ultrasound Scan ◽

Ultrasound Scanning ◽

Eye Muscle ◽

Angus Cattle ◽

Breeding Objective ◽

Two Systems

Abstract The improvement of carcass traits is an important breeding objective in beef cattle breeding programs. The most common way of selecting for improvement in carcass traits is via indirect selection using ultrasound scanning of selection candidates which are submitted to genetic evaluation programs. Two systems used to analyse ultrasound images to predict carcass traits are the Pie Medical Esaote Aquila (PIE) and Central Ultrasound Processing (CUP). This study compared the ability of the two systems to predict carcass traits for genetic evaluation in Australian Angus cattle. Genetic and phenotypic parameters were estimated using data from 1648 Angus steers which were ultrasound scanned twice with both systems, first at feedlot entry and then following 100 days in the feedlot. The traits interpreted from ultrasound scanning included eye muscle area (EMA), rib fat (RIB) rump fat (RUMP) and intramuscular fat (IMF). Abattoir carcass data were collected on all steers following the full feedlot feeding period of 285 days. For all ultrasound scan traits, CUP resulted in higher phenotypic and genetic variances compared to the PIE. For IMF, CUP had higher heritability at feedlot intake (0.51 for CUP compared to 0.37 for PIE) and after 100 days feeding (0.54 for CUP compared to 0.45 PIE). CUP predicted IMF also tended to have stronger correlations with the breeding objective traits of carcass IMF marbling traits, both genetically (ranging from 0.59 to 0.75 for CUP compared to 0.45 to 0.63 for PIE) and phenotypically (ranging from 0.27 to 0.43 for CUP compared to 0.19 to 0.28 for PIE). Ultrasound scan EMA were the only group of traits in which the heritabilities were higher for PIE (0.52 for PIE compared to 0.40 for CUP at feedlot intake and 0.46 for PIE compared to 0.43 for CUP at 100 days of feeding), however with similar relationships to the breeding objective carcass EMA observed. For subcutaneous fat traits of ultrasound RIB and RUMP, the heritabilites and genetic correlations to the related carcass traits were similar, with the exception being the higher heritability observed for CUP predicted RUMP at feedlot intake at 0.52 compared to 0.38 for PIE. The results from this study indicates that the CUP system, compared to PIE, provides an advantage for genetic evaluation of carcass traits in Angus cattle, particularly for the IMF and associated marbling traits.

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