Flat lesion detection of white light cystoscopy with deep learning

Jia，Xiao1; Shkolyar，Eugene2,3; Eminaga，Okyaz2; Laurie，Mark A.1,2; Zhou，Zixia1; Lee，Timothy2,3; Islam，Md Tauhidul1; Meng，Max Q.H.4,5; Liao，Joseph C.2,3; Xing，Lei1

10.1117/12.2650583

2023

Conference on Advanced Photonics in Urology

1605-7422

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

12353

JAN 28-29, 2023

null,San Francisco,CA

1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA

SPIE-INT SOC OPTICAL ENGINEERING

Adequate lesion detection is critical in cystoscopy to improve bladder cancer resection and decrease recurrence. In particular, flat-appearing cancer such as carcinoma in situ is difficult to discern by standard white-light cystoscopy (WLC). The adoption of blue-light cystoscopy (BLC), an adjunct imaging technique, remains modest due to the expensive equipment required. We developed a deep-learning algorithm, CystoNet-F, for augmented detection of flat lesions on WLC. CystoNet-F was designed to augment WLC in lesion detection by incorporating domain translation of CycleGAN, transfer learning, and region of interest (ROI) detection. We constructed a development dataset of 40 patients for algorithm training and 10 patients for testing. In the training phase, features from both WLC and BLC were learned and embedded in the algorithm as the model weights of an ROI detector. Transfer learning was performed by fine-tuning CystoNet-F on BLC using the weights learned from WLC. We applied CycleGAN for domain translation between WLC and BLC. In the test phase, WLC input was first translated to the BLC domain and then served as the input of the ROI detector to finally generate a mask on the lesion area. CystoNet-F can produce flat lesion predictions close to urologist’s annotations on the validation set without paired BLC information. The proposed deep-learning algorithm may improve the diagnostic yield of standard WLC in a noninvasive and cost-effective fashion.

bladder cancer CystoNet-F deep learning flat lesion detection White-light cystoscopy

其他

英语

CPCI-S ; EI

National Institutes of Health[R01 CA260426];

Optics ; Urology & Nephrology

Optics ; Urology & Nephrology

WOS:001011708200012

20232114128086

Cost effectiveness ; Deep learning ; Diseases ; Endoscopy ; Image segmentation ; Medical imaging ; Statistical tests

Biomedical Engineering:461.1 ; Ergonomics and Human Factors Engineering:461.4 ; Medicine and Pharmacology:461.6 ; Machine Learning:723.4.2 ; Imaging Techniques:746 ; Industrial Economics:911.2 ; Mathematical Statistics:922.2

2-s2.0-85159684929

Scopus

1.Department of Radiation Oncology,Stanford University,Stanford,United States
2.Department of Urology,Stanford University,Stanford,United States
3.VA Palo Alto Health Care System,Palo Alto,United States
4.Department of Electronic Engineering,The Chinese University of Hong Kong,Hong Kong,Hong Kong
5.Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen,China

Jia，Xiao,Shkolyar，Eugene,Eminaga，Okyaz,et al. Flat lesion detection of white light cystoscopy with deep learning[C]. 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA:SPIE-INT SOC OPTICAL ENGINEERING,2023.