What question did this study set out to answer?

The aim is to improve kidney tumor segmentation accuracy using deep learning techniques to tackle challenges in conventional methods.

May 13, 2026Open Access

MDA-Net: A Segmentation Network for Kidney Tumor Based on Enhanced Multi-Scale Feature Extraction and Attention Refinement

Key Points

The aim is to improve kidney tumor segmentation accuracy using deep learning techniques to tackle challenges in conventional methods.
Proposed MDA-Net incorporates Multi-Scale Feature Extraction and attention modules.
Used encoder-decoder architecture with skip connections for enhanced feature integration.
Performed experiments on KiTS19 and KiTS21 datasets with detailed ablation studies.
MDA-Net improved tumor boundary delineation and lesion localization over baseline methods (specific metrics not provided).
Demonstrated stronger robustness and cross-dataset generalizability than existing techniques.
Grad-CAM visualizations enhanced clinical interpretability of segmentation results.

Abstract

Accurate kidney tumor segmentation from abdominal CT is essential for quantitative assessment and treatment planning. However, indistinct tumor boundaries and substantial inter-patient shape variability render traditional hand-crafted feature-based methods unreliable for precise delineation. Although deep learning has advanced this task, these methods still struggle with multi-scale tumor characteristics, complex morphological variations, and background noise in medical images. To address these challenges, we propose MDA-Net, an end-to-end segmentation method based on enhanced multi-scale feature extraction and attention refinement. Specifically, we introduce a Multi-Scale Feature Extraction (MSFE) module into encoder–decoder skip connections to aggregate dilated features across multiple receptive fields and learn branch-wise weights for adaptive refinement and fusion, thereby enhancing boundary details and semantic cues to reduce tumor-tissue ambiguity. At the bottleneck, a Deformable Pyramid Feature Refinement (DPFR) module combines deformable sampling with pyramid contextual modeling, thereby improving adaptability to variations in tumor shape and scale while preserving feature resolution. Moreover, a Channel and Spatial Attention (CASA) module is embedded in the decoder to suppress background interference and enhance boundary-sensitive structures during upsampling via coordinated channel and spatial reweighting, thereby improving the reconstruction of fine-grained tumor morphology and contours. Experiments on both KiTS19 and KiTS21 show that MDA-Net consistently improves tumor boundary delineation, lesion localization, and mask reconstruction, demonstrating stronger robustness and cross-dataset generalizability than representative baseline methods. Ablation studies further confirm the complementary effects of MSFE, DPFR, and CASA. In addition, Grad-CAM visualizations improve the clinical transparency and interpretability of the model. Overall, this method advances deep learning for medical image analysis and supports precise diagnosis and treatment of renal tumors.

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