Deep learning-based segmentation of epithelial ovarian cancer on T2-weighted magnetic resonance images

Abstract

Background: Epithelial ovarian cancer (EOC) segmentation is an indispensable step in assessing the extent of disease and guiding the treatment plan that follows. Currently, manual segmentation is the most commonly used method, despite it being tedious, time-consuming and subject to inter-and intra-observer variability. This study aims to assess the feasibility of deep learning methods in the automatic segmentation of EOC on T2-weighted magnetic resonance images. Methods: A total of 339 EOC patients from eight different clinical centers were enrolled and divided into 4 groups: training set (n=154), validation set (n=25), internal test set (n=50) and external test set (n=110). Six common-used algorithms, including convolutional neural networks (CNNs) (U-Net, DeepLabv3, U-Net++ and PSPNet) and transformers (TransUnet and Swin-Unet), were used to conduct automatic segmentations. The performances of these automatic segmentation methods were evaluated by means of dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetric surface distance (ASSD), precision and recall.Results: All the results look promising, which demonstrates the feasibility of using deep learning for EOC segmentation. Overall, CNNs and transformers showed similar performances in both internal and external test sets. Among all the models, U-Net++ performed best with a DSC, HD, ASSD, precision and

Deep learning-based PET image denoising and reconstruction: a review

“…In addition to adopting a network, such as a U-Net, which is capable of image-to-image translation as a generator, it can be regarded as a training method that considers the adversarial loss based on the output from the discriminator. GAN training proceeds such that the label data are no longer distinguishable from the output images of the CNN, thereby synthesizing denoised PET images with less spatial blur and better visual quality [114][115][116]. Common models for denoising by GANs include Conditional GAN [117] and Pix2Pix [118], while incorporating various network structures [119,120] and additional loss functions, such as least squares [121,122], task-specific perceptual loss [123], pixelwise loss [124], and Wasserstein distance with a gradient penalty [125], have all been reported to improve denoising performance.…”

Semisupervised 3D segmentation of pancreatic tumors in positron emission tomography/computed tomography images using a mutual information minimization and cross-fusion strategy

“…In addition, we observed that tumor segmentation on the head and neck cancer dataset was significantly more challenging compared to the pancreatic cancer dataset due to the significant variability in the shape, size, and location of head and neck cancer tumors, as they can occur in various locations within the head and neck region ( 4 ). Moreover, the head and neck cancer dataset came from five different medical centers, leading to differences in collection and quality, along with the presence of lymph nodes with high metabolic responses in PET images ( 56 ). These factors increased the difficulty of accurately segmenting the head and neck tumors.…”

“…This delineation was then validated and confirmed by another radiologist with more than 10 years of experience in pancreatic disease diagnosis. Importantly, both radiologists were kept blind to the patients’ clinical outcomes ( 56 , 57 ). The pancreatic cancer datasets in this study were all confirmed by histopathological or cytological examination.…”

3D Printing for Cancer Diagnosis: What Unique Advantages Are Gained?

“…Cancer is characterized by the uncontrolled growth and spread of abnormal cells within the body , Early detection plays a crucial role in improving patient outcomes and survival rates. Traditional diagnostic methods, such as imaging techniques (e.g., X-ray, computed tomography, , magnetic resonance imaging , ) and biopsy, have been instrumental in cancer diagnosis. However, these methods often provide limited information about the molecular and genetic characteristics of tumors, which are crucial for personalized treatment decisions. − …”