O-Net: An Overall Convolutional Network for Segmentation Tasks.

ABSTRACT: Convolutional neural networks (CNNs) have recently been popular for classification and segmentation through numerous network architectures offering a substantial performance improvement. Their value has been particularly appreciated in the domain of biomedical applications, where even a small improvement in the predicted segmented region (e.g., a malignancy) compared to the ground truth can potentially lead to better diagnosis or treatment planning. Here, we introduce a novel architecture, namely the Overall Convolutional Network (O-Net), which takes advantage of different pooling levels and convolutional layers to extract more deeper local and containing global context. Our quantitative results on 2D images from two distinct datasets show that O-Net can achieve a higher dice coefficient w

SUBMITTER: Maghsoudi OH 

PROVIDER: S-EPMC8286447 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature