Artificial intelligence for segmentation of bladder tumor cystoscopic images performed by U-Net with dilated convolution

Jun Mutaguchi, Ken Ichi Morooka, Satoshi Kobayashi, Aiko Umehara, Shoko Miyauchi, Fumio Kinoshita, Junichi Inokuchi, Yoshinao Oda, Ryo Kurazume, Masatoshi Eto

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Early intravesical recurrence after transurethral resection of bladder tumors (TURBT) is often caused by overlooking of tumors during TURBT. Although narrow-band imaging and photodynamic diagnosis were developed to detect more tumors than conventional white-light imaging, the accuracy of these systems has been subjective, along with poor reproducibility due to their dependence on the physician's experience and skills. To create an objective and reproducible diagnosing system, we aimed to assess the utility of artificial intelligence (AI) with Dilated U-Net to reduce the risk of overlooked bladder tumors when compared with the conventional AI system, termed U-Net.

MATERIAL AND METHODS: We retrospectively obtained cystoscopic images by converting videos obtained from 120 patients who underwent TURBT into 1,790 cystoscopic images. The Dilated U-Net, which is an extension of the conventional U-Net, analyzed these image datasets. The diagnostic accuracy of the Dilated U-Net and conventional U-Net were compared using the following four measurements: pixel-wise sensitivity (PWSe); pixel-wise specificity (PWSp); pixel-wise positive predictive value (PWPPV), representing the AI diagnostic accuracy per pixel; and dice similarity coefficient (DSC), representing the overlap area between the bladder tumors in the ground truth images and segmentation maps.

RESULTS: The cystoscopic images were divided as follows, according to the pathological T-stage: 944, Ta; 412, T1; 329, T2; and 116, carcinoma in-situ. The PWSe, PWSp, PWPPV, and DSC of the Dilated U-Net were 84.9%, 88.5%, 86.7%, and 83.0%, respectively, which had improved when compared to that with the conventional U-Net by 1.7%, 1.3%, 2.1%, and 2.3%, respectively. The DSC values were high for elevated lesions and low for flat lesions for both Dilated and conventional U-Net.

CONCLUSIONS: Dilated U-Net, with higher DSC values than conventional U-Net, might reduce the risk of overlooking bladder tumors during cystoscopy and TURBT.

Original languageEnglish
JournalJournal of Endourology
DOIs
Publication statusE-pub ahead of print - Jan 12 2022

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