Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

An Entire Renal Anatomy Extraction Network for Advanced CAD During Partial Nephrectomy

Partial nephrectomy (PN) is common surgery in urology. Digitization of renal anatomies brings much help to many computer-aided diagnosis (CAD) techniques during PN. However, the manual delineation of kidney vascular system and tumor on each slice is time consuming, error-prone, and inconsistent. Therefore, we proposed an entire renal anatomies extraction method from Computed Tomographic Angiographic (CTA) images fully based on deep learning. We adopted a coarse-to-fine workflow to extract target tissues: first, we roughly located the kidney region, and then cropped the kidney region for more detail extraction. The network we used in our workflow is based on 3D U-Net. To dealing with the imbalance of class contributions to loss, we combined the dice loss with focal loss, and added an extra weight to prevent excessive attention. We also improved the manual annotations of vessels by merging semi-trained model's prediction and original annotations under supervision. We performed several experiments to find the best-fitting combination of variables for training. We trained and evaluated the models on our 60 cases dataset with 3 different sources. The average dice score coefficient (DSC) of kidney, tumor, cyst, artery, and vein, were 90.9%, 90.0%, 89.2%, 80.1% and 82.2% respectively. Our modulate weight and hybrid strategy of loss function increased the average DSC of all tissues about 8-20%. Our optimization of vessel annotation improved the average DSC about 1-5%. We proved the efficiency of our network on renal anatomies segmentation. The high accuracy and fully automation make it possible to quickly digitize the personal renal anatomies, which greatly increases the feasibility and practicability of CAD application on urology surgery

Prospects of deep learning for medical imaging

Machine learning techniques are essential components of medical imaging research. Recently, a highly flexible machine learning approach known as deep learning has emerged as a disruptive technology to enhance the performance of existing machine learning techniques and to solve previously intractable problems. Medical imaging has been identified as one of the key research fields where deep learning can contribute significantly. This review article aims to survey deep learning literature in medical imaging and describe its potential for future medical imaging research. First, an overview of how traditional machine learning evolved to deep learning is provided. Second, a survey of the application of deep learning in medical imaging research is given. Third, wellknown software tools for deep learning are reviewed. Finally, conclusions with limitations and future directions of deep learning in medical imaging are provided

Case series of breast fillers and how things may go wrong: radiology point of view

INTRODUCTION: Breast augmentation is a procedure opted by women to overcome sagging breast due to breastfeeding or aging as well as small breast size. Recent years have shown the emergence of a variety of injectable materials on market as breast fillers. These injectable breast fillers have swiftly gained popularity among women, considering the minimal invasiveness of the procedure, nullifying the need for terrifying surgery. Little do they know that the procedure may pose detrimental complications, while visualization of breast parenchyma infiltrated by these fillers is also deemed substandard; posing diagnostic challenges. We present a case series of three patients with prior history of hyaluronic acid and collagen breast injections. REPORT: The first patient is a 37-year-old lady who presented to casualty with worsening shortness of breath, non-productive cough, central chest pain; associated with fever and chills for 2-weeks duration. The second patient is a 34-year-old lady who complained of cough, fever and haemoptysis; associated with shortness of breath for 1-week duration. CT in these cases revealed non thrombotic wedge-shaped peripheral air-space densities. The third patient is a 37‐year‐old female with right breast pain, swelling and redness for 2- weeks duration. Previous collagen breast injection performed 1 year ago had impeded sonographic visualization of the breast parenchyma. MRI breasts showed multiple non- enhancing round and oval shaped lesions exhibiting fat intensity. CONCLUSION: Radiologists should be familiar with the potential risks and hazards as well as limitations of imaging posed by breast fillers such that MRI is required as problem-solving tool