TY - JOUR ID - 154823 TI - Mass Detection in Automated Three Dimensional Breast Ultrasound using Improved Inception 3D U-Net JO - Journal of Machine Vision and Image Processing JA - JMVIP LA - en SN - AU - Barekatrezaei, Sepideh AU - Malekmohammadi, Amin AU - Kozegar, Ehsan AU - Salamati, Masoumeh AU - Soryani, Mohsen AD - PhD. Student of Computer Engineering, Iran University of Science and Technology, Tehran, Iran AD - MsC. Student Computer Engineering, Iran University of Science and Technology, Tehran, Iran AD - Dept. of Engineering, University of Guilan, Guilan, Iran AD - AD - Dept. of Computer Engineering, Science and Technology University of Iran, Tehran, Iran Y1 - 2023 PY - 2023 VL - 10 IS - 1 SP - 49 EP - 59 KW - Automated three-dimensional breast ultrasound KW - Computer-Aided Detection KW - 3D convolutional neural network KW - Mass Detection KW - Inception DO - N2 - Breast cancer is the leading cause of cancer death among women in most countries. Early detection of breast cancer has a significant effect on reducing mortality. Automated three-dimensional breast ultrasound (3D ABUS) is a type of imaging that has recently been used alongside mammography for the early detection of breast cancer. The 3D volume includes many slices. The radiologist will have to look at all the slices to find the mass, which is time-consuming with a high probability of mistakes. Today, many computer-aided detection (CAD) systems have been proposed to help radiologists in mass detection.In this paper, the 3D U-Net architecture is improved by placing two types of modified Inception modules in the encoder and used to detect masses in 3D ABUS imahges. In the first Inception module, which is located in the first layer of the encoder, various three-dimensional features with two different fields of view are generated. In the second module, which is placed in the following layers of the encoder, line-wise features and plane-wise features are extracted. The dataset contains 60 3D ABUS volumes from 43 patients and includes 55 masses. The proposed network achieves a sensitivity of 92.9% and a false-positive per patient of 22.75 UR - https://jmvip.sinaweb.net/article_154823.html L1 - https://jmvip.sinaweb.net/article_154823_7b63ce84567f6607adb5e76ab6daac18.pdf ER -