Hyper Vision Net: Kidney Tumor Segmentation Using Coordinate Convolutional Layer and Attention Unit
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.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderSegmentationSemantic SegmentationTumor SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation
Kidney tumor segmentation emerges as a new frontier of computer vision in medical imaging. This is partly due to its challenging manual annotation and great medical impact. Within the scope of the Kidney Tumor Segmentati…
SegmentationTumor SegmentationMulti Scale Supervised 3D U-Net for Kidney and Tumor Segmentation
U-Net has achieved huge success in various medical image segmentation challenges. Kinds of new architectures with bells and whistles might succeed in certain dataset when employed with optimal hyper-parameter, but their …
DecoderImage SegmentationMedical Image SegmentationSemantic Segmentation+1Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation
Automated medical image segmentation is a priority research area for computational methods. In particular, detection of cancerous tumors represents a current challenge in this area with potential for real-world impact. T…
Computed Tomography (CT)Image SegmentationMedical Image SegmentationSegmentation+23D Kidneys and Kidney Tumor Semantic Segmentation using Boundary-Aware Networks
Automated segmentation of kidneys and kidney tumors is an important step in quantifying the tumor's morphometrical details to monitor the progression of the disease and accurately compare decisions regarding the kidney t…
DecoderSegmentationSemantic SegmentationTumor SegmentationBoundary-Aware Network for Kidney Parsing
Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical imag…
DecoderImage SegmentationMedical Image SegmentationSegmentation+1