AI-Driven Robust Kidney and Renal Mass Segmentation and Classification on 3D CT Images

Early intervention in kidney cancer helps to improve survival rates. Abdominal computed tomography (CT) is often used to diagnose renal masses. In clinical practice, the manual segmentation and quantification of organs and tumors are expensive and time-consuming. Artificial intelligence (AI) has sho...

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Bibliographic Details
Published in:Bioengineering (Basel) 2023-01, Vol.10 (1), p.116
Main Authors: Liu, Jingya, Yildirim, Onur, Akin, Oguz, Tian, Yingli
Format: Article
Language:English
Subjects:
Abdomen
Algorithms
Annotations
Artificial intelligence
Bioengineering
Biopsy
Cancer
Classification
Computed tomography
Datasets
Deep learning
Diagnosis
Image processing
Image retrieval
Image segmentation
kidney and renal mass segmentation
Kidney cancer
Kidneys
Machine learning
Medical imaging
Medical research
Methods
Neural networks
Renal cell carcinoma
renal mass classification
Robustness
Semantics
Supervised learning
Tumors
weakly supervised learning
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Summary:Early intervention in kidney cancer helps to improve survival rates. Abdominal computed tomography (CT) is often used to diagnose renal masses. In clinical practice, the manual segmentation and quantification of organs and tumors are expensive and time-consuming. Artificial intelligence (AI) has shown a significant advantage in assisting cancer diagnosis. To reduce the workload of manual segmentation and avoid unnecessary biopsies or surgeries, in this paper, we propose a novel end-to-end AI-driven automatic kidney and renal mass diagnosis framework to identify the abnormal areas of the kidney and diagnose the histological subtypes of renal cell carcinoma (RCC). The proposed framework first segments the kidney and renal mass regions by a 3D deep learning architecture (Res-UNet), followed by a dual-path classification network utilizing local and global features for the subtype prediction of the most common RCCs: clear cell, chromophobe, oncocytoma, papillary, and other RCC subtypes. To improve the robustness of the proposed framework on the dataset collected from various institutions, a weakly supervised learning schema is proposed to leverage the domain gap between various vendors via very few CT slice annotations. Our proposed diagnosis system can accurately segment the kidney and renal mass regions and predict tumor subtypes, outperforming existing methods on the KiTs19 dataset. Furthermore, cross-dataset validation results demonstrate the robustness of datasets collected from different institutions trained via the weakly supervised learning schema.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering10010116