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Advanced Driver Assistance Systems (ADAS) refer to the set of smart components such as object detection and tracking systems used to protect drivers and road users. An important task that should be performed by ADAS is the measurement of the distance between a vehicle and a detected object. This helps to anticipate the position of objects in the lane, prevents collisions and improves the safety on the road. For this purpose, Distance Measurement Systems (DMS) including Visionbased techniques, Millimeter wave radars, Infrared ranging, and LIDAR are used in the automotive industry. In this paper, we discuss the design of such systems and their use in smart vehicles. Particular attention is given to vision-based techniques because they are considered among the most accurate systems used to identify targets and to measure the distance from them to a vehicle.

With the success of VTK and ITK, there are more attentions to the toolkit development issue in medical imaging community. This paper introduces MITK, an integrated medical image processing and analyzing toolkit. Its main purpose is to provide a consistent framework to combine the function of medical image segmentation, registration and visualization. The design goals, overall framework and implementation of some key technologies are provided in details, and some application examples are also given to demonstrate the ability of MITK. We hope that MITK will become another available choice for the medical imaging community.

Background The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images. Methods Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups ( n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR−), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists. Results The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87–0.91, 0.89–0.92, 0.87–0.91, and 0.86–0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups ( P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%). Conclusions The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists. Trial registration Chictr.org , ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139 .