Medical image segmentation plays a vital role in medical imaging with a broad spectrum of applications. Popular medical image segmentation tasks include tumor delineation, organ and cell segmentation, volume measurement. The primary objective of medical image segmentation is to partition an image into different segments to study the anatomical structures and identify the region of interest. This technique helps in extracting region of interest from medical images, thereby enhancing the accuracy and efficiency of medical analysis and interventions.
Prostate Segmentation on Micro Ultrasound Images
MicroSegNet is a clinically practical segmentation tool which has the potential to assist urologists in prostate biopsy and cancer diagnosis, offering valuable support in clinical decision-making.
2.5D Medical Image Segmentation Approach
CSA-Net achieves state of the art image registration of images with significant displacement or scaling deformations.
CIS-UNET: Multi-class Aortic Segmentation
Novel context-aware shifted window self-attention block for efficient segmentation of subtle, complex, and heterogeneous objects.
Automated Deep Learning Aortic Zone Segmentation Model
A deep learning model was created to assess volumetric growth in patients with acute uncomplicated type B aortic dissection, marking a significant advancement in medical imaging analysis.
