Convolutional neural network- based pelvic floor structure segmentation using magnetic resonance imaging in pelvic organ prolapse

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162690/2/mp14377.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162690/1/mp14377_am.pd

DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks

In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the well-known GrabCut method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naive approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy

U-Net and its variants for medical image segmentation: theory and applications

U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.Comment: 42 pages, in IEEE Acces

Automatic Segmentation of Human Placenta Images with U-Net

© 2013 IEEE. Placenta is closely related to the health of the fetus. Abnormal placental function will affect the normal development of the fetus, and in severe cases, even endanger the life of the fetus. Therefore, accurate and quantitative evaluation of placenta has important clinical significance. It is a common method to segment human placenta with semantic segmentation. However, manual segmentation relies too much on the professional knowledge and clinical experience of the staff, and it will also consume a lot of time. Therefore, based on u-net, we propose an automatic segmentation method of human placenta, which reduces manual intervention and greatly speeds up the segmentation, making large-scale segmentation possible. The human placenta data set we used was labeled by experts, which was obtained from prenatal examinations of 11 pregnant women, about 1,110 images. It was a comprehensive and clinically significant data set. By training the network with such data set, the robustness of the model will be better. After testing on the data set, the segmentation effect is basically consistent with the manual segmentation effect

MRI Image Segmentation System of Uterine Fibroids Based on AR-Unet Network

Uterine fibroids are the most common benign tumors in female reproductive organs. The segmentation of uterine fibroids is crucial for accurate treatment. This paper proposes a new uterine fibroids MRI T2W image segmentation network AR-Unet (Attention Resnet101-Unet), which uses the deep neural network ResNet101 as the front end of feature extraction, extracts image semantic information, and combines U-net design ideas to build a network structure. The attention gate module is added before the upsampling and downsampling feature maps are spliced. We tested a total of 123 uterine fibroids MRI T2W images from 13 patients. The segmentation results were verified with expert-defined manual segmentation results. The average Dice coefficient, IOU value, sensitivity and specificity of all segmented images were 0.9044, 0.8443, 88.55% and 94.56%, the performance is better than ResNet101-Unet and Attention-Unet models, and finally the network is encapsulated into an auxiliary diagnostic system

Anatomical Segmentation of CT images for Radiation Therapy planning using Deep Learning

Radiation therapy is one of the key cancer treatment options. To avoid adverse effect in tissue surrounding the tumor, the treatment plan needs to be based on accurate anatomical models of the patient. In this thesis, an automatic segmentation solution is constructed for the female breast, the female pelvis and the male pelvis using deep learning. The deep neural networks applied performed as well as the current state of the art networks while improving inference speed by a factor of 15 to 45. The speed increase was gained through processing the whole 3D image at once. The segmentations done by clinicians usually take several hours, whereas the automatic segmentation can be done in less than a second. Therefore, the automatic segmentation provides options for adaptive treatment planning