Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000× larger in size

A Survey on Thyroid Ultrasound Image Analysis

Ultrasound imaging plays a prominent role in the diagnosis of thyroid gland.Imaging helps to detect and classify the abnormalities of thyroid gland.This survey focuses on thyroid ultrasound image features that are important for diagnosis.Various researchers have developed different techniques to detect and classify the thyroid nodules.A brief survey of various techniques developed for the analysis of thyroid ultrasound images is carried out in this paper

Quantitative assessment of wound healing using high-frequency ultrasound image analysis

Purpose: We aimed to develop a method for quantitative assessment of wound healing in ulcerated diabetic feet. Methods: High‐frequency ultrasound (HFU) images of 30 wounds were acquired in a controlled environment on post‐debridement days 7, 14, 21, and 28. Meaningful features portraying changes in structure and intensity of echoes during healing were extracted from the images, their relevance and discriminatory power being verified by analysis of variance. Relative analysis of tissue healing was conducted by developing a features‐based healing function, optimised using the pattern‐search method. Its performance was investigated through leave‐one‐out cross‐validation technique and reconfirmed using principal component analysis. Results: The constructed healing function could depict tissue changes during healing with 87.8% accuracy. The first principal component derived from the extracted features demonstrated similar pattern to the constructed healing function, accounting for 86.3% of the data variance. Conclusion: The developed wound analysis technique could be a viable tool in quantitative assessment of diabetic foot ulcers during healing

Thyroid nodule ultrasound image analysis and feature extraction

In this study, I introduce a novel workflow for extracting useful features in thyroid ultrasound images using deep learning and machine learning methods. The methodology combines Convolutional Auto-Encoder, Local Binary Patterns, Histogram of Oriented Gradients and professional image characterization together to extract useful information from medical images. Multiple machine learning classifiers are used to build an effective thyroid tumor diagnosis model from extracted features. The experimental results show that Support Vector Machine with a specifically designed preprocessing scheme and a customized objective function outperforms human on the test set. The final model can effectively reduce the number of unnecessary biopsies and the number of missing malignancies

Assesment of Stroke Risk Based on Morphological Ultrasound Image Analysis With Conformal Prediction

Non-invasive ultrasound imaging of carotid plaques allows for the development of plaque image analysis in order to assess the risk of stroke. In our work, we provide reliable confidence measures for the assessment of stroke risk, using the Conformal Prediction framework. This framework provides a way for assigning valid confidence measures to predictions of classical machine learning algorithms. We conduct experiments on a dataset which contains morphological features derived from ultrasound images of atherosclerotic carotid plaques, and we evaluate the results of four different Conformal Predictors (CPs). The four CPs are based on Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Naive Bayes classification (NBC), and k-Nearest Neighbours (k-NN). The results given by all CPs demonstrate the reliability and usefulness of the obtained confidence measures on the problem of stroke risk assessment

Sonographic Appearance of Abdominal Wall at the Left Flank of Laparotomy Incision Site in Ettawah Grade Does

The aim of this study was to describe the sonographic appearance of abdominal wall at the left flank of laparotomy incision site in 11 mated Ettawah grade does. Brightness-mode ultrasound examination by using transducer with frequency of 5.0-6.0 MHz was conducted to grouping the does based on their pregnancy statuses. The incision site of the abdominal wall at left flank laparotomy was transcutaneous-scanned as long as 8 cm vertically. The sonographic appearance of the laparotomy wall thickness showed that in all groups of does were similar and not different statistically. The thickness of oblique external and oblique internal abdominal muscles increased in the pregnant does as compared to non-pregnant does (P<0.05)