Semiautomatic segmentation for prostate brachytherapy: Dosimetric evaluation

Focal application of low-dose-rate brachytherapy for prostate cancer: a pilot study

Purpose

To evaluate the feasibility and to report the early outcomes of focal treatment of prostate cancer using low-dose-rate brachytherapy (LDR-PB).

Material and methods

Seventeen patients were screened with multi-parametric magnetic resonance imaging (mpMRI), 14 of whom proceeded to receive trans-perineal template mapping biopsy (TTMB). Focal LDR-PB was performed on five eligible patients using dual air kerma strength treatment plans based on planning target volumes derived from cancer locations and determined by TTMB. Patient follow-up includes prostate specific antigen (PSA) measurements, urinary and sexual function questionnaires, repeated imaging and TTMB at specific intervals post-treatment.

Results

Feasibility of focal LDR-PB was shown and short-term outcomes are promising. While the detection rate of tumors, a majority of which were low grade GS 3 + 3, was found to be low on mpMRI (sensitivity of 37.5%), our results suggest the potential of mpMRI in detecting the presence of higher grade (GS ≥ 3 + 4), and bilateral disease indicating its usefulness as a screening tool for focal LDR-PB.

Conclusions

Low-dose-rate brachytherapy is a favorable ablation option for focal treatment of prostate cancer, requiring minimal modification to the standard (whole gland) LDR-PB treatment, and appears to have a more favorable side effect profile. Further investigation, in the form of a larger study, is needed to assess the methods used and the long-term outcomes of focal LDR-PB.

Postediting prostate magnetic resonance imaging segmentation consistency and operator time using manual and computer-assisted segmentation: multiobserver study

Prostate segmentation on T2w MRI is important for several diagnostic and therapeutic procedures for prostate cancer. Manual segmentation is time-consuming, labor-intensive, and subject to high interobserver variability. This study investigated the suitability of computer-assisted segmentation algorithms for clinical translation, based on measurements of interoperator variability and measurements of the editing time required to yield clinically acceptable segmentations. A multioperator pilot study was performed under three pre- and postediting conditions: manual, semiautomatic, and automatic segmentation. We recorded the required editing time for each segmentation and measured the editing magnitude based on five different spatial metrics. We recorded average editing times of 213, 328, and 393 s for manual, semiautomatic, and automatic segmentation respectively, while an average fully manual segmentation time of 564 s was recorded. The reduced measured postediting interoperator variability of semiautomatic and automatic segmentations compared to the manual approach indicates the potential of computer-assisted segmentation for generating a clinically acceptable segmentation faster with higher consistency. The lack of strong correlation between editing time and the values of typically used error metrics ([Formula: see text]) implies that the necessary postsegmentation editing time needs to be measured directly in order to evaluate an algorithm's suitability for clinical translation.

Prostate contours delineation using interactive directional active contours model and parametric shape prior model

Prostate contours delineation on Magnetic Resonance (MR) images is a challenging and important task in medical imaging with applications of guiding biopsy, surgery and therapy. While a fully automated method is highly desired for this application, it can be a very difficult task due to the structure and surrounding tissues of the prostate gland. Traditional active contours-based delineation algorithms are typically quite successful for piecewise constant images. Nevertheless, when MR images have diffuse edges or multiple similar objects (e.g. bladder close to prostate) within close proximity, such approaches have proven to be unsuccessful. In order to mitigate these problems, we proposed a new framework for bi-stage contours delineation algorithm based on directional active contours (DAC) incorporating prior knowledge of the prostate shape. We first explicitly addressed the prostate contour delineation problem based on fast globally DAC that incorporates both statistical and parametric shape prior model. In doing so, we were able to exploit the global aspects of contour delineation problem by incorporating a user feedback in contours delineation process where it is shown that only a small amount of user input can sometimes resolve ambiguous scenarios raised by DAC. In addition, once the prostate contours have been delineated, a cost functional is designed to incorporate both user feedback interaction and the parametric shape prior model. Using data from publicly available prostate MR datasets, which includes several challenging clinical datasets, we highlighted the effectiveness and the capability of the proposed algorithm. Besides, the algorithm has been compared with several state-of-the-art methods.

A multi-atlas-based segmentation framework for prostate brachytherapy

Low-dose-rate brachytherapy is a radiation treatment method for localized prostate cancer. The standard of care for this treatment procedure is to acquire transrectal ultrasound images of the prostate in order to devise a plan to deliver sufficient radiation dose to the cancerous tissue. Brachytherapy planning involves delineation of contours in these images, which closely follow the prostate boundary, i.e., clinical target volume. This process is currently performed either manually or semi-automatically, which requires user interaction for landmark initialization. In this paper, we propose a multi-atlas fusion framework to automatically delineate the clinical target volume in ultrasound images. A dataset of a priori segmented ultrasound images, i.e., atlases, is registered to a target image. We introduce a pairwise atlas agreement factor that combines an image-similarity metric and similarity between a priori segmented contours. This factor is used in an atlas selection algorithm to prune the dataset before combining the atlas contours to produce a consensus segmentation. We evaluate the proposed segmentation approach on a set of 280 transrectal prostate volume studies. The proposed method produces segmentation results that are within the range of observer variability when compared to a semi-automatic segmentation technique that is routinely used in our cancer clinic.