Ground truth free retinal vessel segmentation by learning from simple pixels

Retinal Blood Vessel Segmentation with Optic Disc Pixels Exclusion

The morphological changes of retinal blood vessels are important indicators used to diagnose and monitor the progression of various diseases. A number of retinal blood vessel segmentation methods have been introduced, including the line operator based methods, which have shown satisfactory results. However, the basic line operator method cannot discriminate the pixels around the retinal optic d...

Automatic Retinal Vessel Segmentation

Diabetic Retinopathy is the most common cause of blindness in the working population of the western world and is very common among people who suffer from diabetes. Fortunately, during a clinical examination an ophthalmologist is able to determine the onset of the disease by taking certain features of the retinal vessels of the fundus into account. These features include the narrowing of vessels...

Blood Vessel Segmentation from Retinal Images

Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth

A lack of generalizability is one key limitation of deep learning based segmentation. Typically, one manually labels new training images when segmenting organs in different imaging modalities or segmenting abnormal organs from distinct disease cohorts. The manual efforts can be alleviated if one is able to reuse manual labels from one modality (e.g., MRI) to train a segmentation network for a n...

Can Ground Truth Label Propagation from Video Help Semantic Segmentation?

For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves extensive human effort. In this work, we study the possibility of using auxiliary ground truth, so-called pseudo ground truth (PGT) to improve the performance. The PGT is obtained by propagating the labels of a GT fra...