Multi‐atlas–based auto‐segmentation for prostatic urethra using novel prediction of deformable image registration accuracy

• Published in: Medical physics (Lancaster) 2020-07, Vol.47 (7), p.3023-3031
• Main Authors: Takagi, Hisamichi, Kadoya, Noriyuki, Kajikawa, Tomohiro, Tanaka, Shohei, Takayama, Yoshiki, Chiba, Takahito, Ito, Kengo, Dobashi, Suguru, Takeda, Ken, Jingu, Keiichi
• Format: Article
• Language: English
• Subjects: Algorithms; auto‐segmentation; deformable image registration; Humans; Image Processing, Computer-Assisted; machine learning; Male; prostate cancer; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - radiotherapy; radiotherapy; Radiotherapy Planning, Computer-Assisted; Radiotherapy, Intensity-Modulated; Tomography, X-Ray Computed; Urethra - diagnostic imaging
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.14154

Purpose Accurate identification of the prostatic urethra and bladder can help determine dosing and evaluate urinary toxicity during intensity‐modulated radiation therapy (IMRT) planning in patients with localized prostate cancer. However, it is challenging to locate the prostatic urethra in planning computed tomography (pCT). In the present study, we developed a multiatlas–based auto‐segmentation method for prostatic urethra identification using deformable image registration accuracy prediction with machine learning (ML) and assessed its feasibility. Methods We examined 120 patients with prostate cancer treated with IMRT. All patients underwent temporary urinary catheter placement for identification and contouring of the prostatic urethra in pCT images (ground truth). Our method comprises the following three steps: (a) select four atlas datasets from the atlas datasets using the deformable image registration (DIR) accuracy prediction model, (b) deform them by structure‐based DIR, (3) and propagate urethra contour using displacement vector field calculated by the DIR. In (a), for identifying suitable datasets, we used the trained support vector machine regression (SVR) model and five feature descriptors (e.g., prostate volume) to increase DIR accuracy. This method was trained/validated using 100 patients and performance was evaluated within an independent test set of 20 patients. Fivefold cross‐validation was used to optimize the hype parameters of the DIR accuracy prediction model. We assessed the accuracy of our method by comparing it with those of two others: Acostas method‐based patient selection (previous study method, by Acosta et al.), and the Waterman’s method (defines the prostatic urethra based on the center of the prostate, by Waterman et al.). We used the centerlines distance (CLD) between the ground truth and the predicted prostatic urethra as the evaluation index. Results The CLD in the entire prostatic urethra was 2.09 ± 0.89 mm (our proposed method), 2.77 ± 0.99 mm (Acosta et al., P = 0.022), and 3.47 ± 1.19 mm (Waterman et al., P