U-Net++DSM: Improved U-Net++ for Brain Tumor Segmentation With Deep Supervision Mechanism

Abstract

The segmentation of brain tumors is an important and challenging content in medical image processing. Relying solely on human experts to manually segment large volumes of data can be timeconsuming and delay diagnosis. To address this challenge, researchers have set out to develop an algorithm that can automatically determine whether MRI images contain brain tumors and identify their features. This paper proposes the U-Net++DSM, a collaborative approach combining U-Net++ with Deep Supervision Mechanism (DSM) as its backbone. To enhance the segmentation power of U-Net++DSM, medical professionals have trained a dilation operator using fully annotated images. The results of this method demonstrate that the combination of U-Net++DSM and the dilation operator significantly improves segmentation accuracy, especially when the number of fully-labeled images is limited. The results show that the proposed U-Net++DSM outperforms traditional U-Net models by achieving high segmentation performance, surpassing other state-of-the-art models, with a sensitivity of 98.59.00%, a specificity of 98.72%, an accuracy of 98.64%, and an average Dice score of 98.81% when tested on publicly available databases. Compared to other existing segmentation methods, the U-Net++DSM method has the potential to yield even better brain tumor segmentation results in terms of pixel-based classification and dice similarity performance metrics.

Retinal Image Registration Using Partial Intensity Invariant Feature Descriptor and Redundant Keypoints Elimination Techniques

“…The disk structuring element with a diameter of 7 pixels is selected for this stage, and 4) the retinal image enhancement process involves transforming the enhanced image into a binary format. This is done by applying an optimal threshold based on the desired outcome [30], [31]. This step aims to obtain a clear and precise visualization of the vascular structure in the retina.…”