Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit

TL;DR

This paper introduces Hyper Vision Net, a deep learning model with coordinate convolution and attention mechanisms, achieving superior kidney tumor segmentation accuracy on CT images compared to existing methods.

Contribution

The novel Hyper Vision Net architecture incorporates supervision layers and attention units, improving segmentation accuracy over traditional U-net models.

Findings

Achieved a dice score of 0.9552 for tumor segmentation.

Achieved a dice score of 0.9633 for kidney segmentation.

Outperformed state-of-the-art methods on KiTs19 dataset.

Abstract

KiTs19 challenge paves the way to haste the improvement of solid kidney tumor semantic segmentation methodologies. Accurate segmentation of kidney tumor in computer tomography (CT) images is a challenging task due to the non-uniform motion, similar appearance and various shape. Inspired by this fact, in this manuscript, we present a novel kidney tumor segmentation method using deep learning network termed as Hyper vision Net model. All the existing U-net models are using a modified version of U-net to segment the kidney tumor region. In the proposed architecture, we introduced supervision layers in the decoder part, and it refines even minimal regions in the output. A dataset consists of real arterial phase abdominal CT scans of 300 patients, including 45964 images has been provided from KiTs19 for training and validation of the proposed model. Compared with the state-of-the-art segmentation methods, the results demonstrate the superiority of our approach on training dice value score of 0.9552 and 0.9633 in tumor region and kidney region, respectively.