Application of Deep Learning in Cancer Prognosis Prediction Model.

As an important branch of artificial intelligence and machine learning, deep learning (DL) has been widely used in various aspects of cancer auxiliary diagnosis, among which cancer prognosis is the most important part. High-accuracy cancer prognosis is beneficial to the clinical management of patients with cancer. Compared with other methods, DL models can significantly improve the accuracy of prediction.

GAN-based metal artifacts region inpainting in brain MRI imaging with reflective registration.

Background And Objective: Metallic magnetic resonance imaging (MRI) implants can introduce magnetic field distortions, resulting in image distortion, such as bulk shifts and signal-loss artifacts. Metal Artifacts Region Inpainting Network (MARINet), using the symmetry of brain MRI images, has been developed to generate normal MRI images in the image domain and improve image quality.

Methods: T1-weighted MRI images containing or located near the teeth of 100 patients were collected.

Inpainting truncated areas of CT images based on generative adversarial networks with gated convolution for radiotherapy.

This study aimed to inpaint the truncated areas of CT images by using generative adversarial networks with gated convolution (GatedConv) and apply these images to dose calculations in radiotherapy. CT images were collected from 100 patients with esophageal cancer under thermoplastic membrane placement, and 85 cases were used for training based on randomly generated circle masks. In the prediction stage, 15 cases of data were used to evaluate the accuracy of the inpainted CT in anatomy and dosimetry based on the mask with a truncated volume covering 40% of the arm volume, and they were compared with the inpainted CT synthesized by U-Net, pix2pix, and PConv with partial convolution.

New technique and application of truncated CBCT processing in adaptive radiotherapy for breast cancer.

Objective: A generative adversarial network (TCBCTNet) was proposed to generate synthetic computed tomography (sCT) from truncated low-dose cone-beam computed tomography (CBCT) and planning CT (pCT). The sCT was applied to the dose calculation of radiotherapy for patients with breast cancer.

Methods: The low-dose CBCT and pCT images of 80 female thoracic patients were used for training.

Inpainting the metal artifact region in MRI images by using generative adversarial networks with gated convolution.

Purpose: Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis, but it is susceptible to metal artifacts. The generative adversarial network GatedConv with gated convolution (GC) and contextual attention (CA) was used to inpaint the metal artifact region in MRI images.

Methods: MRI images containing or near the teeth of 70 patients were collected, and the scanning sequence was a T1-weighted high-resolution isotropic volume examination sequence.

Research on new treatment mode of radiotherapy based on pseudo-medical images.

Background And Objective: Multi-modal medical images with multiple feature information are beneficial for radiotherapy. A new radiotherapy treatment mode based on triangle generative adversarial network (TGAN) model was proposed to synthesize pseudo-medical images between multi-modal datasets.

Methods: CBCT, MRI and CT images of 80 patients with nasopharyngeal carcinoma were selected.

TNSNet: Thyroid nodule segmentation in ultrasound imaging using soft shape supervision.

Background And Objectives: Thyroid nodules are a common disorder of the endocrine system. Segmentation of thyroid nodules on ultrasound images is an important step in the evaluation and diagnosis of nodules and an initial step in computer-aided diagnostic systems. The accuracy and consistency of segmentation remain a challenge due to the low contrast, speckle noise, and low resolution of ultrasound images.

Synthesis of pseudo-CT images from pelvic MRI images based on an MD-CycleGAN model for radiotherapy.

A multi-discriminator-based cycle generative adversarial network (MD-CycleGAN) model is proposed to synthesize higher-quality pseudo-CT from MRI images.MRI and CT images obtained at the simulation stage with cervical cancer were selected to train the model. The generator adopted DenseNet as the main architecture.