Non-Parametric Bayesian Registration (NParBR) of Body Tumors in DCE-MRI Data

Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction

Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%.

An Automatic Detection of Liver Tumor from CT Abdominal Images—A Comparative Approach

The liver is a vital organ in human body. Liver performs an important function including metabolism, digestion, and detoxification. Liver is a significant organ in an abdomen, and is connected to the nearby organ such as spleen, pancreas, gallbladder, abdomen, and gut through blood vessels. Specific approaches such as image gradient and region growing are not quite reliable for the segmentation of the liver tumor. A level-set approach is evaluated in this paper compared with the active contour approach of segmentation of the liver imaging from the image of the CT abdomen and Unified level set method, spatial Fuzzy C-means method for segmenting tumor from segmented liver images is appraised. The proposed approach is implemented by using the 3DIRCADB dataset available to the public as well as non-public datasets taken from Arthi Hospital, Chennai and Tirunelveli scanning centre. For validating the system based on the diverse quantitative measures, including space overlap, coefficient of similarity, Jaccard indices, using ground truth images, which are available in the public data set 3DIRCADB and the expert segmentation results which are manually identified by the clinical partner for nonpublic datasets. The analysis of the algorithm shows the better results for segmenting liver using level set system and spatial segmentation of Fuzzy C means of the tumor segmentation.