ArticleEURASIP journal on image and video processing2022
Automatic kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for malignant potential analysis in complex renal cyst based on CT images.
Article in EURASIP journal on image and video processing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 37 citations in OpenAlex.
- Advanced kidney mass segmentation using VHUCS-Net with protuberance detection network.Frontiers in artificial intelligence · 2026Article
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- KidneyNeXt: A Lightweight Convolutional Neural Network for Multi-Class Renal Tumor Classification in Computed Tomography Imaging.Journal of clinical medicine · 2025Article
- AI-Assisted Image Recognition of Cervical Spine Vertebrae in Dynamic X-Ray Recordings.Bioengineering (Basel, Switzerland) · 2025Article
- CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.Sensors (Basel, Switzerland) · 2024Article
- ChineseMPD: A Semantic Segmentation Dataset of Chinese Martial Arts Classic Movie Props.Scientific data · 2024Article
- Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI).Quantitative imaging in medicine and surgery · 2024Article
- Accurate exclusion of kidney regions affected by susceptibility artifact in blood oxygenation level-dependent (BOLD) images using deep-learning-based segmentation.Scientific reports · 2023Article
- 2.5D MFFAU-Net: a convolutional neural network for kidney segmentation.BMC medical informatics and decision making · 2023Article
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2 authors at 1 institution in 1 country.
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Abstract
Bosniak renal cyst classification has been widely used in determining the complexity of a renal cyst. However, it turns out that about half of patients undergoing surgery for Bosniak category III, take surgical risks that reward them with no clinical benefit at all. This is because their pathological results reveal that the cysts are actually benign not malignant. This problem inspires us to use recently popular deep learning techniques and study alternative analytics methods for precise binary classification (benign or malignant tumor) on Computerized Tomography (CT) images. To achieve our goal, two consecutive steps are required-segmenting kidney organs or lesions from CT images then classifying the segmented kidneys. In this paper, we propose a study of kidney segmentation using 2.5D ResUNet and 2.5D DenseUNet for efficiently extracting intra-slice and inter-slice features. Our models are trained and validated on the public data set from Kidney Tumor Segmentation (KiTS19) challenge in two different training environments. As a result, all experimental models achieve high mean kidney Dice scores of at least 95% on the KiTS19 validation set consisting of 60 patients. Apart from the KiTS19 data set, we also conduct separate experiments on abdomen CT images of four Thai patients. Based on the four Thai patients, our experimental models show a drop in performance, where the best mean kidney Dice score is 87.60%.
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