A deep learning framework for prostate localization in cone beam CT-guided radiotherapy

Abstract

Purpose: To develop a deep learning-based model for prostate planning target volume (PTV) localization on cone beam computed tomography (CBCT) to improve the workflow of CBCT-guided patient setup.

Methods: A two-step task-based residual network (T² RN) is proposed to automatically identify inherent landmarks in prostate PTV. The input to the T² RN is the pretreatment CBCT images of the patient, and the output is the deep learning-identified landmarks in the PTV. To ensure robust PTV localization, the T² RN model is trained by using over thousand sets of CT images with labeled landmarks, each of the CTs corresponds to a different scenario of patient position and/or anatomy distribution generated by synthetically changing the planning CT (pCT) image. The changes, including translation, rotation, and deformation, represent vast possible clinical situations of anatomy variations during a course of radiation therapy (RT). The trained patient-specific T² RN model is tested by using 240 CBCTs from six patients. The testing CBCTs consists of 120 original CBCTs and 120 synthetic CBCTs. The synthetic CBCTs are generated by applying rotation/translation transformations to each of the original CBCT.

Results: The systematic/random setup errors between the model prediction and the reference are found to be <0.25/2.46 mm and 0.14/1.41° in translation and rotation dimensions, respectively. Pearson's correlation coefficient between model prediction and the reference is higher than 0.94 in translation and rotation dimensions. The Bland-Altman plots show good agreement between the two techniques.

Conclusions: A novel T² RN deep learning technique is established to localize the prostate PTV for RT patient setup. Our results show that highly accurate marker-less prostate setup is achievable by leveraging the state-of-the-art deep learning strategy.

Keywords: CBCT; IGRT; deep learning; localization; prostate; radiotherapy.

Figure 1.

Network architecture of the proposed T²RN for prostate setup. (a) the 1^st step for prostate center localization. (b) the 2^nd step for landmarks detection. Abbreviation: pCT = planning computed tomography, T²RN = Two-step Task-based Residual Network.

Figure 2.

Examples of the 3D landmarks estimated from the proposed T²RN model (colored in yellow) and their corresponding reference (colored in red) in the 3D rendering. The columns (a), (b) and (c) show the results in three patients, respectively.

Figure 3.

The difference of couch shifts between the model prediction and the reference on the 240 CBCT scans of different treatment fractions of six patients. (a) and (b) are the results in translation and rotation dimensions, respectively.

Figure 4.

Analysis of the difference of couch shifts between the model prediction and the reference on the 240 CBCT scans of different treatment fractions of six patients. The first row shows the histograms of the difference. The second row shows the Bland-Altman analysis with the predefined clinically accepted tolerance (solid line) and the 95% confidence interval (dot-dash line). The third row shows the CC. (1–6) show the results in the direction of A-P, L-R, S-I, yaw, pitch, and roll, respectively. Abbreviation: A-P = anterior-posterior, L-R = left-right, S-I = superior-inferior.

Figure 5.

The checkerboard of the FMs-free images without (a), with the conventional (b), and with the proposed (c) prostate alignment. Row 1 and 2 shows the result of the fraction #1 and #2, respectively. The relative motion between bony anatomy and prostate in fraction #1 is small, while fraction #2 is large.