Oblique needle segmentation and tracking for 3D TRUS guided prostate brachytherapy

A Narrative Review of Image Processing Techniques Related to Prostate Ultrasound

Prostate cancer (PCa) poses a significant threat to men's health, with early diagnosis being crucial for improving prognosis and reducing mortality rates. Transrectal ultrasound (TRUS) plays a vital role in the diagnosis and image-guided intervention of PCa. To facilitate physicians with more accurate and efficient computer-assisted diagnosis and interventions, many image processing algorithms in TRUS have been proposed and achieved state-of-the-art performance in several tasks, including prostate gland segmentation, prostate image registration, PCa classification and detection and interventional needle detection. The rapid development of these algorithms over the past 2 decades necessitates a comprehensive summary. As a consequence, this survey provides a narrative review of this field, outlining the evolution of image processing methods in the context of TRUS image analysis and meanwhile highlighting their relevant contributions. Furthermore, this survey discusses current challenges and suggests future research directions to possibly advance this field further.

Medical instrument detection in ultrasound: a review

Medical instrument detection is essential for computer-assisted interventions, since it facilitates clinicians to find instruments efficiently with a better interpretation, thereby improving clinical outcomes. This article reviews image-based medical instrument detection methods for ultrasound-guided (US-guided) operations. Literature is selected based on an exhaustive search in different sources, including Google Scholar, PubMed, and Scopus. We first discuss the key clinical applications of medical instrument detection in the US, including delivering regional anesthesia, biopsy taking, prostate brachytherapy, and catheterization. Then, we present a comprehensive review of instrument detection methodologies, including non-machine-learning and machine-learning methods. The conventional non-machine-learning methods were extensively studied before the era of machine learning methods. The principal issues and potential research directions for future studies are summarized for the computer-assisted intervention community. In conclusion, although promising results have been obtained by the current (non-) machine learning methods for different clinical applications, thorough clinical validations are still required.

Three-dimensional therapy needle applicator segmentation for ultrasound-guided focal liver ablation

PURPOSE

Minimally invasive procedures, such as microwave ablation, are becoming first-line treatment options for early-stage liver cancer due to lower complication rates and shorter recovery times than conventional surgical techniques. Although these procedures are promising, one reason preventing widespread adoption is inadequate local tumor ablation leading to observations of higher local cancer recurrence compared to conventional procedures. Poor ablation coverage has been associated with two-dimensional (2D) ultrasound (US) guidance of the therapy needle applicators and has stimulated investigation into the use of three-dimensional (3D) US imaging for these procedures. We have developed a supervised 3D US needle applicator segmentation algorithm using a single user input to augment the addition of 3D US to the current focal liver tumor ablation workflow with the goals of identifying and improving needle applicator localization efficiency.

METHODS

The algorithm is initialized by creating a spherical search space of line segments around a manually chosen seed point that is selected by a user on the needle applicator visualized in a 3D US image. The most probable trajectory is chosen by maximizing the count and intensity of threshold voxels along a line segment and is filtered using the Otsu method to determine the tip location. Homogeneous tissue mimicking phantom images containing needle applicators were used to optimize the parameters of the algorithm prior to a four-user investigation on retrospective 3D US images of patients who underwent microwave ablation for liver cancer. Trajectory, axis localization, and tip errors were computed based on comparisons to manual segmentations in 3D US images.

RESULTS

Segmentation of needle applicators in ten phantom 3D US images was optimized to median (Q1, Q3) trajectory, axis, and tip errors of 2.1 (1.1, 3.6)°, 1.3 (0.8, 2.1) mm, and 1.3 (0.7, 2.5) mm, respectively, with a mean ± SD segmentation computation time of 0.246 ± 0.007 s. Use of the segmentation method with a 16 in vivo 3D US patient dataset resulted in median (Q1, Q3) trajectory, axis, and tip errors of 4.5 (2.4, 5.2)°, 1.9 (1.7, 2.1) mm, and 5.1 (2.2, 5.9) mm based on all users.

CONCLUSIONS

Segmentation of needle applicators in 3D US images during minimally invasive liver cancer therapeutic procedures could provide a utility that enables enhanced needle applicator guidance, placement verification, and improved clinical workflow. A semi-automated 3D US needle applicator segmentation algorithm used in vivo demonstrated localization of the visualized trajectory and tip with less than 5° and 5.2 mm errors, respectively, in less than 0.31 s. This offers the ability to assess and adjust needle applicator placements intraoperatively to potentially decrease the observed liver cancer recurrence rates associated with current ablation procedures. Although optimized for deep and oblique angle needle applicator insertions, this proposed workflow has the potential to be altered for a variety of image-guided minimally invasive procedures to improve localization and verification of therapy needle applicators intraoperatively.

A Robotic Ultrasound Scanner for Automatic Vessel Tracking and Three-Dimensional Reconstruction of B-Mode Images

Locating and evaluating the length and severity of a stenosis is very important for planning adequate treatment of peripheral arterial disease (PAD). Conventional ultrasound (US) examination cannot provide maps of entire lower limb arteries in 3-D. We propose a prototype 3D-US robotic system with B-mode images, which is nonionizing, noninvasive, and is able to track and reconstruct a continuous segment of the lower limb arterial tree between the groin and the knee. From an initialized cross-sectional view of the vessel, automatic tracking was conducted followed by 3D-US reconstructions evaluated using Hausdorff distance, cross-sectional area, and stenosis severity in comparison with 3-D reconstructions with computed tomography angiography (CTA). A mean Hausdorff distance of 0.97 ± 0.46 mm was found in vitro for 3D-US compared with 3D-CTA vessel representations. To evaluate the stenosis severity in vitro, 3D-US reconstructions gave errors of 3%-6% when compared with designed dimensions of the phantom, which are comparable to 3D-CTA reconstructions, with 4%-13% errors. The in vivo system's feasibility to reconstruct a normal femoral artery segment of a volunteer was also investigated. These results encourage further ergonomic developments to increase the robot's capacity to represent lower limb vessels in the clinical context.

Phase grouping-based needle segmentation in 3-D trans-rectal ultrasound-guided prostate trans-perineal therapy

A robust and efficient needle segmentation method used to localize and track the needle in 3-D trans-rectal ultrasound (TRUS)-guided prostate therapy is proposed. The algorithmic procedure begins by cropping the 3-D US image containing a needle; then all voxels in the cropped 3-D image are grouped into different line support regions (LSRs) based on the outer product of the adjacent voxels' gradient vector. Two different needle axis extraction methods in the candidate LSR are presented: least-squares fitting and 3-D randomized Hough transform. Subsequent local optimization refines the position of the needle axis. Finally, the needle endpoint is localized by finding an intensity drop along the needle axis. The proposed methods were validated with 3-D TRUS tissue-mimicking agar phantom images, chicken breast phantom images and patient images obtained during prostate cryotherapy. The results of the in vivo test indicate that our method can localize the needle accurately and robustly with a needle endpoint localization accuracy <1.43 mm and detection accuracy >84%, which are favorable for 3-D TRUS-guided prostate trans-perineal therapy.

Automatic shape-based level set segmentation for needle tracking in 3-D TRUS-guided prostate brachytherapy

Prostate brachytherapy is an effective treatment for early prostate cancer. The success depends critically on the correct needle implant positions. We have devised an automatic shape-based level set segmentation tool for needle tracking in 3-D transrectal ultrasound (TRUS) images, which uses the shape information and level set technique to localize the needle position and estimate the endpoint of needle in real-time. The 3-D TRUS images used in the evaluation of our tools were obtained using a 2-D TRUS transducer from Ultrasonix (Richmond, BC, Canada) and a computer-controlled stepper motor system from Thorlabs (Newton, NJ, USA). The accuracy and feedback mechanism had been validated using prostate phantoms and compared with 3-D positions of these needles derived from experts' readings. The experts' segmentation of needles from 3-D computed tomography images was the ground truth in this study. The difference between automatic and expert segmentations are within 0.1 mm for 17 of 19 implanted needles. The mean errors of automatic segmentations by comparing with the ground truth are within 0.25 mm. Our automated method allows real-time TRUS-based needle placement difference within one pixel compared with manual expert segmentation.

Three-dimensional ultrasound scanning

The past two decades have witnessed developments of new imaging techniques that provide three-dimensional images about the interior of the human body in a manner never before available. Ultrasound (US) imaging is an important cost-effective technique used routinely in the management of a number of diseases. However, two-dimensional viewing of three-dimensional anatomy, using conventional two-dimensional US, limits our ability to quantify and visualize the anatomy and guide therapy, because multiple two-dimensional images must be integrated mentally. This practice is inefficient, and may lead to variability and incorrect diagnoses. Investigators and companies have addressed these limitations by developing three-dimensional US techniques. Thus, in this paper, we review the various techniques that are in current use in three-dimensional US imaging systems, with a particular emphasis placed on the geometric accuracy of the generation of three-dimensional images. The principles involved in three-dimensional US imaging are then illustrated with a diagnostic and an interventional application: (i) three-dimensional carotid US imaging for quantification and monitoring of carotid atherosclerosis and (ii) three-dimensional US-guided prostate biopsy.

Contemporary technique of intraoperative 3-dimensional ultrasonography-guided transperineal prostate cryotherapy

Successful cryotherapy of the prostate for neoplasms relies on imaging to achieve good oncological outcomes with minimal complications. Traditional prostatic cryotherapy relies on 2-dimensional ultrasonography (2DUS) guidance, which often makes it difficult to track the passage of needles in an oblique plane. We describe our initial 3-dimensional ultrasonography (3DUS) system, and the subsequent improvements that have been made during the last 10 years. Our imaging system uses a Philips HDI 5000 ultrasonography unit, a standard PC, a Matrox Meteor II video frame grabber and 3DUS developed at Robarts Research Institute. For the cryotherapy we use ultrathin (17-gauge) IceRod needles. After image acquisition, preplanning is performed using the 3-dimensional (3D) software, and then the IceRod needles are inserted into the prostate. As the freezing process commences, continuous 3DUS images are taken and analyzed during the double freeze-thaw cycles to monitor the progress of the ice ball formation. Real-time intraoperative 3D imaging of the prostate during cryotherapy has allowed us to accurately preplan and then monitor the progression of ice ball formation, which represents a significant advantage over conventional 2DUS.

Biopsy needle detection in transrectal ultrasound

Using the fusion of pre-operative MRI and real time intra-procedural transrectal ultrasound (TRUS) to guide prostate biopsy has been shown as a very promising approach to yield better clinical outcome than the routinely performed TRUS only guided biopsy. In several situations of the MRI/TRUS fusion guided biopsy, it is important to know the exact location of the deployed biopsy needle, which is imaged in the TRUS video. In this paper, we present a method to automatically detect and segment the biopsy needle in TRUS. To achieve this goal, we propose to combine information from multiple resources, including ultrasound probe stability, TRUS video background model, and the prior knowledge of needle orientation and position. The proposed algorithm was tested on TRUS video sequences which have in total more than 25,000 frames. The needle deployments were successfully detected and segmented in the sequences with high accuracy and low false-positive detection rate.

Temporal-based needle segmentation algorithm for transrectal ultrasound prostate biopsy procedures

PURPOSE

Automatic identification of the biopsy-core tissue location during a prostate biopsy procedure would provide verification that targets were adequately sampled and would allow for appropriate intraprocedure biopsy target modification. Localization of the biopsy core requires accurate segmentation of the biopsy needle and needle tip from transrectal ultrasound (TRUS) biopsy images. A temporal-based TRUS needle segmentation algorithm was developed specifically for the prostate biopsy procedure to automatically identify the TRUS image containing the biopsy needle from a collection of 2D TRUS images and to segment the biopsy-core location from the 2D TRUS image.

METHODS

The temporal-based segmentation algorithm performs a temporal analysis on a series of biopsy TRUS images collected throughout needle insertion and withdrawal. Following the identification of points of needle insertion and retraction, the needle axis is segmented using a Hough transform-based algorithm, which is followed by a temporospectral TRUS analysis to identify the biopsy-needle tip. Validation of the temporal-based algorithm is performed on 108 TRUS biopsy sequences collected from the procedures of ten patients. The success of the temporal search to identify the proper images was manually assessed, while the accuracies of the needle-axis and needle-tip segmentations were quantitatively compared to implementations of two other needle segmentation algorithms within the literature.

RESULTS

The needle segmentation algorithm demonstrated a >99% accuracy in identifying the TRUS image at the moment of needle insertion from the collection of real-time TRUS images throughout the insertion and withdrawal of the biopsy needle. The segmented biopsy-needle axes were accurate to within 2.3 +/- 2.0 degrees and 0.48 +/- 0.42 mm of the gold standard. Identification of the needle tip to within half of the biopsy-core length (<10 mm) was 95% successful with a mean error of 2.4 +/- 4.0 mm. Needle-tip detection using the temporal-based algorithm was significantly more accurate (p < 0.001) than the other two algorithms tested, while the segmentation of the needle axis was not significantly different between the three algorithms.

CONCLUSIONS

The temporal-based needle segmentation algorithm accurately segments the location of the biopsy core from 2D TRUS images of clinical prostate biopsy procedures. The results for needle-tip localization demonstrated that the temporal-based algorithm is significantly more accurate than implementations of some existing needle segmentation algorithms within the literature.

Prediction of seed migration after transperineal interstitial prostate brachytherapy with I-125 free seeds

PURPOSE

The present study was undertaken to determine the incidence and predictors of seed migration after transperineal interstitial prostate brachytherapy using I-125 free seeds.

METHODS AND MATERIALS

Between September 2004 and November 2007, 158 patients who underwent transperineal interstitial prostate brachytherapy as monotherapy for clinical T1/T2 carcinoma of the prostate gland were reviewed. Implants had been performed with standard techniques. All 158 patients underwent followup radiographs (orthogonal chest radiographs, a kidney-ureter-bladder radiograph, and a posteroanterior pelvic radiograph) to assess the presence of seed migration at 3 months after transperineal interstitial prostate brachytherapy. Patient characteristics and treatment status were recorded. Univariate and multivariate analyses were performed to identify predictors of seed migration.

RESULTS

Seed migration occurred in 35 of 158 patients (22.2%). Univariate analyses revealed that preoperative prostate volume estimated by transrectal ultrasound, the number of needles, the number of seeds implanted, and the presence or absence of pubic arch interference (PAI) were significantly associated with seed migration. These results indicated that larger prostate glands were more likely to have seed migration. However, the absolute difference in prostate size was not overly impressive (22.4 vs. 26.3cm(3)). Multivariate analysis revealed that the number of seeds implanted and the presence or absence of PAI were significant predictors of seed migration.

CONCLUSION

The number of seeds implanted and the presence or absence of PAI provide the most predictive information on seed migration.

Anniversary paper: evolution of ultrasound physics and the role of medical physicists and the AAPM and its journal in that evolution

Ultrasound has been the greatest imaging modality worldwide for many years by equipment purchase value and by number of machines and examinations. It is becoming increasingly the front end imaging modality; serving often as an extension of the physician's fingers. We believe that at the other extreme, high-end systems will continue to compete with all other imaging modalities in imaging departments to be the method of choice for various applications, particularly where safety and cost are paramount. Therapeutic ultrasound, in addition to the physiotherapy practiced for many decades, is just coming into its own as a major tool in the long progression to less invasive interventional treatment. The physics of medical ultrasound has evolved over many fronts throughout its history. For this reason, a topical review, rather than a primarily chronological one is presented. A brief review of medical ultrasound imaging and therapy is presented, with an emphasis on the contributions of medical physicists, the American Association of Physicists in Medicine (AAPM) and its publications, particularly its journal Medical Physics. The AAPM and Medical Physics have contributed substantially to training of physicists and engineers, medical practitioners, technologists, and the public.

Curved multi-tube device for path-error correction in a needle-insertion system

BACKGROUND

Needle insertion is an important procedure for the diagnosis and treatment of various diseases. A prototype curved multi-tube device (CMTD) and system for performing computerized correction of path errors during needle insertion is described.

METHODS

The CMTD provides two functional modes, the straight-needle mode and the curved-needle mode. This study describes the CMTD and path-error correction, using a prototype needle-insertion system in a trial with a simple water phantom.

RESULTS

The needle insertion procedure was performed precisely in the centre of an ultrasound image. The CMTD functioned with the inner needle extending properly from its sleeve to correct path errors.

CONCLUSIONS

The path-error corrections by the CMTD in a needle-insertion system were shown. The system's computer processing can successfully determine the needle path from the ultrasound image and automatically guide the CMTD to the target.

Automated localization of implanted seeds in 3D TRUS images used for prostate brachytherapy

An algorithm has been developed in this paper to localize implanted radioactive seeds in 3D ultrasound images for a dynamic intraoperative brachytherapy procedure. Segmentation of the seeds is difficult, due to their small size in relatively low quality of transrectal ultrasound (TRUS) images. In this paper, intraoperative seed segmentation in 3D TRUS images is achieved by performing a subtraction of the image before the needle has been inserted, and the image after the seeds have been implanted. The seeds are searched in a "local" space determined by the needle position and orientation information, which are obtained from a needle segmentation algorithm. To test this approach, 3D TRUS images of the agar and chicken tissue phantoms were obtained. Within these phantoms, dummy seeds were implanted. The seed locations determined by the seed segmentation algorithm were compared with those obtained from a volumetric cone-beam flat-panel micro-CT scanner and human observers. Evaluation of the algorithm showed that the rms error in determining the seed locations using the seed segmentation algorithm was 0.98 mm in agar phantoms and 1.02 mm in chicken phantoms.