Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep-learning (DL)-based image analysis methodology on pre-treatment diagnostic computed tomography (dCT), low-dose CT (lCT), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images and rule-based reasoning to predict treatment response to chimeric antigen receptor (CAR) T-cell therapy in B-cell lymphomas. Pre-treatment images of 770 lymph node lesions from 39 adult patients with B-cell lymphomas treated with CD19-directed CAR T-cells were analyzed.

CT image crack segmentation method based on linear feature enhancement.

Industrial computed tomography (CT) crack segmentation is a key technology in industrial CT image processing. Unfortunately, the interference of artifact and noise in CT image often bring great trouble to the crack segmentation. In order to improve the segmentation accuracy of cracks in CT images, we propose to develop and test a new crack segmentation algorithm based on linear feature enhancement by analyzing the features of cracks in CT images.

Research on 3D crack segmentation of CT images of oil rock core.

In this paper, we propose a framework for CT image segmentation of oil rock core. According to the characteristics of CT image of oil rock core, the existing level set segmentation algorithm is improved. Firstly, an algorithm of Chan-Vese (C-V) model is carried out to segment rock core from image background.

Fully automated registration of 3D CT data to CAD model for surface deviation measurement.

The aim of this study is to present a fully automated registration algorithm that allows for alignment and errors analysis of the 3D surface model obtained from industrial computed tomography (CT) images with the computer-aided design (CAD) model. First, two pre-processing steps are executed by the algorithm namely, CAD model subdivision and representing models. Next, two improved registration procedures are applied including covariance descriptors-based coarse registration with a novel and automatic calibration, followed by a fine registration technique that utilizes an improved iterative closest points (ICP) algorithm, which is what we proposed with a novel estimation method for registration error.

3D defect segmentation on CT volume data using morphology and resample techniques.

Segmentation of CT volume data is important and useful in non-destructive testing and evaluating. To eliminate the artifacts influence, we propose a new approach of 3D defect segmentation using two steps. First of all, an initial segmentation using 3D morphological method is performed.