Abstract
Accurate segmentation of fine-grained information is an important step in medical image analysis applications. With the development of the encoder-decoder-based networks, various network structures and algorithms have made significant progress in semantic segmentation tasks. This work aims to present a novel high-resolution encoder-decoder network (HRED-Net) for fine-grained image segmentation that is highly accurate for small-scale targets. We design a multiscale context connection module to extract feature information without reducing the resolution, and propose a multiresolution fusion model to fine-tune the final results. In addition, these modules are trained together with a detail-oriented loss function to enhance the model’s perception of fine-grained parts. Through experiments on the DRIVE dataset, we found a balance between these modules, and our comparison results show that in addition to the extraction multiscale features, the fusion of multiresolution prediction information is also beneficial for fine-grained segmentation. Our method yielded significant improvements in the accuracy and sensitivity in retinal vessel and lung segmentation tasks.
Highlights
Focusing on the details of image segmentation is an ongoing challenge, and accurate segmentation of medical images, including shapes, locations, and sizes, provides scientific assistance to doctors for making accurate diagnoses
Details are essential for medical image segmentation
We proposed a multiscale connection encoder-decoder network that focuses on fine-grained parts
Summary
Focusing on the details of image segmentation is an ongoing challenge, and accurate segmentation of medical images, including shapes, locations, and sizes, provides scientific assistance to doctors for making accurate diagnoses. Due to the limitations of the standardization of clinical data collection programs and some manual interventions in the data collection process [14], fine-grained segmentation [15] of medical images is challenging. The first limitation is low tissue contrast: fine-grained targets tend to be similar to background pixel values, causing inconsistencies or disappearance at the extended end. The second limitation is noise interference: due to the similar physical properties at organizational junctions, and flowing tissue fluid, medical images are often accompanied by impurities and uncertainty shadows.
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