Mammography imaging is one of the most demanding imaging modalities from the point of view of the balance between image quality (the visibility of small size and/or low contrast structures) and dose (screening of many asymptomatic people). Therefore, since the introduction of the first dedicated mammographic units, many efforts have been directed to seek the best possible image quality while minimizing patient dose. The performance of automatic exposure control (AEC) is the manifestation of this demand. The theory of AEC includes exposure detection and optimization and also involves some accomplished methodology. This review presents the development and present situation of spectrum optimization, detector evolution, and the way how to accomplish and evaluate AEC methods.
Our country has been using maturity grading method, which was proposed by Grammum in 1979, to evaluate the placental function. However, this method is subjective to consequence because it totally depends on the observation and experiences of clinicians. With the development of ultrasound technology, therefore, we reviewed more novel applications in other aspects of placenta (such as blood flow, vascularization, etc). Over the past years, scholars in the world have done a lot of research around these topics. In this review we introduce placental maturity grading with B-mode ultrasound, placental vascularization qualitative and quantitative analysis with three-dimensional Doppler ultrasound and placental volume measurement, respectively.
Liver cancer is a common type of malignant tumor in digestive system. At present, computed tomography (CT) plays an important role in the diagnosis and treatment of liver cancer. Segmentation of tumor lesions based on CT is thus critical in clinical diagnosis and treatment. Due to the limitations of manual segmentation, such as inefficiency and subjectivity, the automatic and accurate segmentation based on advanced computational techniques is becoming more and more popular. In this review, we summarize the research progress of automatic segmentation of liver cancer lesions based on CT scans. By comparing and analyzing the results of experiments, this review evaluate various methods objectively, so that researchers in related fields can better understand the current research progress of liver cancer segmentation based on CT scans.
The dramatically increasing high-resolution medical images provide a great deal of useful information for cancer diagnosis, and play an essential role in assisting radiologists by offering more objective decisions. In order to utilize the information accurately and efficiently, researchers are focusing on computer-aided diagnosis (CAD) in cancer imaging. In recent years, deep learning as a state-of-the-art machine learning technique has contributed to a great progress in this field. This review covers the reports about deep learning based CAD systems in cancer imaging. We found that deep learning has outperformed conventional machine learning techniques in both tumor segmentation and classification, and that the technique may bring about a breakthrough in CAD of cancer with great prospect in the future clinical practice.