Deformable registration-based segmentation of the bowel on Megavoltage CT during pelvic radiotherapy

Implementing cone-beam computed tomography-guided online adaptive radiotherapy in cervical cancer

Background and purpose

Adaptive radiotherapy (ART) in locally advanced cervical cancer (LACC) has shown promising outcomes. This study investigated the feasibility of cone-beam computed tomography (CBCT)-guided online ART (oART) for the treatment of LACC.

Material and methods

The quality of the automated radiotherapy treatment plans and artificial intelligence (AI)-driven contour delineation for LACC on a novel CBCT-guided oART system were assessed. Dosimetric analysis of 200 simulated oART sessions were compared with standard treatment. Feasibility of oART was assessed from the delivery of 132 oART fractions for the first five clinical LACC patients. The simulated and live oART sessions compared a fixed planning target volume (PTV) margin of 1.5 cm around the uterus-cervix clinical target volume (CTV) with an internal target volume-based approach. Workflow timing measurements were recorded.

Results

The automatically-generated 12-field intensity-modulated radiotherapy plans were comparable to manually generated plans. The AI-driven organ-at-risk (OAR) contouring was acceptable requiring, on average, 12.3 min to edit, with the bowel performing least well and rated as unacceptable in 16 % of cases. The treated patients demonstrated a mean PTV D98% (+/-SD) of 96.7 (+/- 0.2)% for the adapted plans and 94.9 (+/- 3.7)% for the non-adapted scheduled plans (p<10^-5). The D2cc (+/-SD) for the bowel, bladder and rectum were reduced by 0.07 (+/- 0.03)Gy, 0.04 (+/-0.05)Gy and 0.04 (+/-0.03)Gy per fraction respectively with the adapted plan (p <10^-5). In the live.setting, the mean oART session (+/-SD) from CBCT acquisition to beam-on was 29 +/- 5 (range 21-44) minutes.

Conclusion

CBCT-guided oART was shown to be feasible with dosimetric benefits for patients with LACC. Further work to analyse potential reductions in PTV margins is ongoing.

Autosegmentation of the rectum on megavoltage image guidance scans

Autosegmentation of image guidance (IG) scans is crucial for streamlining and optimising delivered dose calculation in radiotherapy. By accounting for interfraction motion, daily delivered dose can be accumulated and incorporated into automated systems for adaptive radiotherapy. Autosegmentation of IG scans is challenging due to poorer image quality than typical planning kilovoltage computed tomography (kVCT) systems, and the resulting reduction of soft tissue contrast in regions such as the pelvis makes organ boundaries less distinguishable. Current autosegmentation solutions generally involve propagation of planning contours to the IG scan by deformable image registration (DIR). Here, we present a novel approach for primary autosegmentation of the rectum on megavoltage IG scans acquired during prostate radiotherapy, based on the Chan-Vese algorithm. Pre-processing steps such as Hounsfield unit/intensity scaling, identifying search regions, dealing with air, and handling the prostate, are detailed. Post-processing features include identification of implausible contours (nominally those affected by muscle or air), 3D self-checking, smoothing, and interpolation. In cases where the algorithm struggles, the best estimate on a given slice may revert to the propagated kVCT rectal contour. Algorithm parameters were optimised systematically for a training cohort of 26 scans, and tested on a validation cohort of 30 scans, from 10 patients. Manual intervention was not required. Comparing Chan-Vese autocontours with contours manually segmented by an experienced clinical oncologist achieved a mean Dice Similarity Coefficient of 0.78 (SE < 0.011). This was comparable with DIR methods for kVCT and CBCT published in the literature. The autosegmentation system was developed within the VoxTox Research Programme for accumulation of delivered dose to the rectum in prostate radiotherapy, but may have applicability to further anatomical sites and imaging modalities.

Registration methods in radiotherapy

Purpose

The aim of this study is to present a short and comprehensive review of the methods of medical image registration, their conditions and applications in radiotherapy. A particular focus was placed on the methods of deformable image registration.

Methods

To structure and deepen the knowledge on medical image registration in radiotherapy, a medical literature analysis was made using the Google Scholar browser and the medical database of the PubMed library.

Results

Chronological review of image registration methods in radiotherapy based on 34 selected articles. A particular attention was given to show: (i) potential regions of the application of different methods of registration, (ii) mathematical basis of the deformable methods and (iii) the methods of quality control for the registration process.

Conclusions

The primary aim of the medical image registration process is to connect the contents of images. What we want to achieve is a complementary or extended knowledge that can be used for more precise localisation of pathogenic lesions and continuous improvement of patient treatment. Therefore, the choice of imaging mode is dependent on the type of clinical study. It is impossible to visualise all anatomical details or functional changes using a single modality machine. Therefore, fusion of various modality images is of great clinical relevance. A natural problem in analysing the fusion of medical images is geographical errors related to displacement. The registered images are performed not at the same time and, very often, at different respiratory phases.

Segmentation of parotid glands from registered CT and MR images

PURPOSE

To develop an automatic multimodal method for segmentation of parotid glands (PGs) from pre-registered computed tomography (CT) and magnetic resonance (MR) images and compare its results to the results of an existing state-of-the-art algorithm that segments PGs from CT images only.

METHODS

Magnetic resonance images of head and neck were registered to the accompanying CT images using two different state-of-the-art registration procedures. The reference domains of registered image pairs were divided on the complementary PG regions and backgrounds according to the manual delineation of PGs on CT images, provided by a physician. Patches of intensity values from both image modalities, centered around randomly sampled voxels from the reference domain, served as positive or negative samples in the training of the convolutional neural network (CNN) classifier. The trained CNN accepted a previously unseen (registered) image pair and classified its voxels according to the resemblance of its patches to the patches used for training. The final segmentation was refined using a graph-cut algorithm, followed by the dilate-erode operations.

RESULTS

Using the same image dataset, segmentation of PGs was performed using the proposed multimodal algorithm and an existing monomodal algorithm, which segments PGs from CT images only. The mean value of the achieved Dice overlapping coefficient for the proposed algorithm was 78.8%, while the corresponding mean value for the monomodal algorithm was 76.5%.

CONCLUSIONS

Automatic PG segmentation on the planning CT image can be augmented with the MR image modality, leading to an improved RT planning of head and neck cancer.

Medical image segmentation based on level set and isoperimetric constraint

Level set based methods are being increasingly used in image segmentation. In these methods, various shape constraints can be incorporated into the energy functionals to obtain the desired shapes of the contours represented by their zero level sets of functions. Motivated by the isoperimetric inequality in differential geometry, we propose a segmentation method in which the isoperimetric constrain is integrated into a level set framework to penalize the ratio of its squared perimeter to its enclosed area of an active contour. The new model can ensure the compactness of segmenting objects and complete missing or/and blurred parts of their boundaries simultaneously. The isoperimetric shape constraint is free of explicit expressions of shapes and scale-invariant. As a result, the proposed method can handle various objects with different scales and does not need to estimate parameters of shapes. Our method can segment lesions with blurred or/and partially missing boundaries in ultrasound, Computed Tomography (CT) and Magnetic Resonance (MR) images efficiently. Quantitative evaluation also confirms that the proposed method can provide more accurate segmentation than two well-known level set methods. Therefore, our proposed method shows potential of accurate segmentation of lesions for applying in diagnoses and surgical planning.

Patient-reported intestinal toxicity from whole pelvis intensity-modulated radiotherapy: First quantification of bowel dose-volume effects

BACKGROUND AND PURPOSE

Intestinal toxicity is commonly experienced during whole-pelvis intensity-modulated radiotherapy (WPRT) for prostate cancer. The aim of the current study was to assess bowel dose-volume relationships for acute patient-reported intestinal symptoms of patients treated with WPRT for prostate cancer.

MATERIALS AND METHODS

Complete data of 206 patients were available; the median dose to pelvic nodes was 51.8Gy (range 50.4-54.4, 1.7-2Gy/fr). Intestinal symptoms were assessed as changes in the Inflammatory Bowel Disease Questionnaire scores relative to the Bowel Domain (IBDQ-B) between baseline and radiotherapy mid-point/end. The 25th percentiles of the most severe worsening from baseline (ΔIBDQ-B) were set as end-points. The impact of bowel loops and sigmoid colon dose-volume/surface parameters as well as selected clinical parameters were investigated using multivariate logistic regression.

RESULTS

Analyses were focused on the four questions showing a median ΔIBDQ-B>0. No dose volume/surface parameters were predictive, other than ΔIBDQ5≥3 (loose stools): when grouping patients according to bowel DVHs (high risk: V20>470cc, V30>245cc, V42>110cc; low risk: all the remaining patients), a two-variable model including high-risk DVH-shape (OR: 9.3) and age (protective, OR: 0.94) was assessed. The model showed good calibration (slope: 1.003, R²=0.92) and was found to be robust after bootstrap-based internal validation.

CONCLUSIONS

Constraining the bowel loops may reduce the risk of loose stools. The risk is higher for younger patients.

Evaluation of Deformable Image Registration-Based Contour Propagation From Planning CT to Cone-Beam CT

Purpose:

We evaluated the performance of organ contour propagation from a planning computed tomography to cone-beam computed tomography with deformable image registration by comparing contours to manual contouring.

Materials and Methods:

Sixteen patients were retrospectively identified based on showing considerable physical change throughout the course of treatment. Multiple organs in the 3 regions (head and neck, prostate, and pancreas) were evaluated. A cone-beam computed tomography from the end of treatment was registered to the planning computed tomography using rigid registration, followed by deformable image registration. The contours were copied on cone-beam computed tomography image sets using rigid registration and modified by 2 radiation oncologists. Contours were compared using Dice similarity coefficient, mean surface distance, and Hausdorff distance.

Results:

The mean physician-to-physician Dice similarity coefficient for all organs was 0.90. When compared to each physician’s contours, the overall mean for rigid was 0.76 (P < .001), and it was improved to 0.79 (P < .001) for deformable image registration. Comparing deformable image registration to physicians resulted in a mean Dice similarity coefficient of 0.77, 0.74, and 0.84 for head and neck, prostate, and pancreas groups, respectively; whereas, the physician-to-physician mean agreement for these sites was 0.87, 0.90, and 0.93 (P < .001, for all sites). The mean surface distance for physician-to-physician contours was 1.01 mm, compared to 2.58 mm for rigid-to-physician contours and 2.24 mm for deformable image registration-to-physician contours. The mean physician-to-physician Hausdorff distance was 11.32 mm, and when compared to any physician’s contours, the mean for rigid and deformable image registration was 12.1 mm and 12.0 mm (P < .001), respectively.

Conclusion:

The physicians had a high level of agreement via the 3 metrics; however, deformable image registration fell short of this level of agreement. The automatic workflows using deformable image registration to deform contours to cone-beam computed tomography to evaluate the changes during treatment should be used with caution.