Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

This photoacoustic imaging (PAI) in medicine paper started with an introduction to PAI and the famous photoacoustic techniques including photoacoustic tomography (PAT), multispectral optoacoustic tomography (MSOT), photoacoustic microscopy (PAM), raster-scan optoacoustic mesoscopy (RSOM), and photoacoustic elastography (PAE). A modest review about noncontact laser ultrasound (LUS), having the advantage of operator-independent image quality, has been also demonstrated. A concise review of most of PAI's medical applications is demonstrated including cancer screening (for breast, thyroid, ovarian, prostate, lung, and skin), tissue oxygenation measurements, brain imaging, imaging-guided surgery (IGS), and the guidance of high intensity focused ultrasound (HIFU). Some safety considerations contributed with medical ultrasound and lasers have been then presented. In conclusion, more scientific and clinical development in the field of PAI is expected, and an increase in approved devices that utilize PAI's techniques in medical applications is also expected to serve wide sectors of medicine, whether diagnostic or therapeutic.

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Background Biomedical photoacoustic imaging (PAI) is a hybrid imaging modality based on the laser-generated ultrasound waves due to the photoacoustic (PA) effect physical phenomenon that has been reported firstly by A. G. Bell in 1880. Numerical modeling based simulation for PA signal generation process in biological tissues helps researchers for decreasing error trials in-vitro and hence decreasing error rates for in-vivo experiments. Numerical modeling methods help in obtaining a rapid modeling procedure comparable to pure mathematics. However, if a proper simplified mathematical model can be founded before applying numerical modeling techniques, it will be a great advantage for the overall numerical model. More scientific theories, equations, and assumptions through the biomedical PA imaging research literature have been proposed trying to mathematically model the complete PA signal generation and propagation process in biological tissues. However, most of them have so complicated details. Hence, the researchers, especially the beginners, will find a hard difficulty to explore and obtain a proper simplified mathematical model describing the process. That’s why this paper is introduced. Methods In this paper we have tried to simplify understanding for the biomedical PA wave’s generation and propagation process, deducing a simplified mathematical model for the whole process. The proposed deduced model is based on three steps: a- pulsed laser irradiance, b- diffusion of light through biological tissue, and c- acoustic pressure wave generation and propagation from the target tissue to the ultrasound transducer surface.