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MB-FSGAN: Joint segmentation and quantification of kidney tumor on CT by the multi-branch feature sharing generative
Yanan Ruan1, Dengwang Li2, Harry Marshall3
1Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250358, China; University of Western Ontario, London ON, Canada.
Abstract:
The segmentation of the kidney tumor and the quantification of its tumor indices (i.e., the center point coordinates, diameter, circumference, and cross-sectional area of the tumor) are important steps in tumor therapy. These quantifies the tumor morphometrical details to monitor disease progression and accurately compare decisions regarding the kidney tumor treatment. However, manual segmentation and quantification is a challenging and time-consuming process in practice and exhibit a high degree of variability within and between operators. In this paper, MB-FSGAN (multi-branch feature sharing generative adversarial network) is proposed for simultaneous segmentation and quantification of kidney tumor on CT. MB-FSGAN consists of multi-scale feature extractor (MSFE), locator of the area of interest (LROI), and feature sharing generative adversarial network (FSGAN). MSFE makes strong semantic information on different scale feature maps, which is particularly effective in detecting small tumor targets. The LROI extracts the region of interest of the tumor, greatly reducing the time complexity of the network. FSGAN correctly segments and quantifies kidney tumors through joint learning and adversarial learning, which effectively exploited the commonalities and differences between the two related tasks. Experiments are performed on CT of 113 kidney tumor patients. For segmentation, MB-FSGAN achieves a pixel accuracy of 95.7%. For the quantification of five tumor indices, the R2 coefficient of tumor circumference is 0.9465. The results show that the network has reliable performance and shows its effectiveness and potential as a clinical tool.
Insights
A new AI model, MB-FSGAN, automates kidney tumor segmentation and quantification from CT scans. This tool accurately measures tumor indices, aiding in treatment decisions and disease monitoring.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Accurate kidney tumor segmentation and quantification are crucial for effective cancer therapy and monitoring disease progression.
- Manual methods are time-consuming, prone to errors, and lack consistency among different operators.
Purpose of the Study:
- To develop and evaluate MB-FSGAN, a novel deep learning model for simultaneous segmentation and quantification of kidney tumors on CT images.
- To improve the efficiency and accuracy of kidney tumor analysis compared to manual approaches.
Main Methods:
- MB-FSGAN utilizes a multi-branch feature extractor (MSFE) for robust feature learning across scales, a region of interest locator (LROI) to reduce computational load, and a feature-sharing adversarial network (FSGAN) for joint task learning.
- The model was trained and validated on CT scans from 113 kidney tumor patients.
Main Results:
- MB-FSGAN achieved a pixel accuracy of 95.7% for kidney tumor segmentation.
- The model demonstrated high reliability in quantifying tumor indices, with an R² coefficient of 0.9465 for tumor circumference.
- The network effectively leverages shared information between segmentation and quantification tasks.
Conclusions:
- MB-FSGAN offers a reliable and effective automated solution for kidney tumor segmentation and quantification using CT imaging.
- The proposed AI tool shows significant potential for clinical application in improving kidney cancer diagnosis and treatment planning.
Related Concept Videos
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01:21External Anatomy of the Kidney
The kidneys are located in the retroperitoneal space on either side of the vertebral column, protected posteriorly by the 11th and 12th ribs. The right kidney sits slightly lower than the left owing to the presence of the liver...
01:12Internal Anatomy of the Kidney
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The kidneys are retroperitoneal organs positioned against the posterior abdominal wall on either side of the spine, roughly between the twelfth thoracic and third lumbar vertebrae. Each kidney is typically 10-12 cm long, 5-6 cm wide, and 3-4 cm thick, weighing about 150 grams.
Renal Cortex
The outermost region of the kidney is the...
01:45Kidney Structure