Synthesis of diagnostic quality cancer pathology images by generative adversarial networks.

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks to synthesize high-resolution pathology images of 10 histological types of cancer, including five cancer types from The Cancer Genome Atlas and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g. rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey (http://gan.aimlab.ca/). © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Design of high energy laser pulse delivery in a multimode fiber for photoacoustic tomography.

In photoacoustic tomography (PAT), delivering high energy pulses through optical fiber is critical for achieving high quality imaging. A fiber coupling scheme with a beam homogenizer is demonstrated for coupling high energy pulses in a single multimode fiber. This scheme can benefit PAT applications that require miniaturized illumination or internal illumination with a small fiber. The beam homogenizer is achieved by using a cross cylindrical lens array, which provides a periodic spatial modulation on the phase of the input light. Thus the lens array acts as a phase grating which diffracts the beam into a 2D diffraction pattern. Both theoretical analysis and experiments demonstrate that the focused beam can be split into a 2D spot array that can reduce the peak power on the fiber tip surface and thus enhance the coupling performance. The theoretical analysis of the intensity distribution of the focused beam is carried out by Fourier optics. In experiments, coupled energy at 48 mJ/pulse and 60 mJ/pulse have been achieved and the corresponding coupling efficiency is 70% and 90% in a 1000-µm and a 1500-µm-core-diameter fiber, respectively. The high energy pulses delivered by the multimode fiber are further tested for PAT imaging in phantoms. PAT imaging of a printed dot array shows a large illumination area of 7 cm2 under 5 mm thick chicken breast tissue. In vivo imaging is also demonstrated on the human forearm. The large improvement in coupling energy can potentially benefit PAT with single fiber delivery to achieve large area imaging and deep penetration detection.

Broadening the detection view of 2D photoacoustic tomography using two linear array transducers.

Although commercial linear array transducers are widely used in clinical ultrasound, their application in photoacoustic tomography (PAT) is still limited due to the limited-view problem that restricts the image quality. In this paper, we propose a simple approach to address the limited-view problem in 2D by using two linear array transducers to receive PAT signal from different orientations. The positions of the two transducers can be adjusted to fit the specific geometry of an imaging site. This approach is made possible by using a new calibration method, where the relative position between the two transducers can be calibrated using ultrasound by transmitting ultrasound wave with one transducer while receiving with the other. The calibration results are then applied in the subsequent PAT imaging to incorporate the detected acoustic signals from both transducers and thereby increase the detection view. In this calibration method, no calibration phantom is required which largely simplifies and shortens the process. The efficacy of the calibration and improvement on the PAT image quality are demonstrated through phantom studies and in vivo imaging.

Towards machine learning-based quantitative hyperspectral image guidance for brain tumor resection.

BACKGROUND: Complete resection of malignant gliomas is hampered by the difficulty in distinguishing tumor cells at the infiltration zone. Fluorescence guidance with 5-ALA assists in reaching this goal. Using hyperspectral imaging, previous work characterized five fluorophores' emission spectra in most human brain tumors. METHODS: In this paper, the effectiveness of these five spectra was explored for different tumor and tissue classification tasks in 184 patients (891 hyperspectral measurements) harboring low- (n = 30) and high-grade gliomas (n = 115), non-glial primary brain tumors (n = 19), radiation necrosis (n = 2), miscellaneous (n = 10) and metastases (n = 8). Four machine-learning models were trained to classify tumor type, grade, glioma margins, and IDH mutation. RESULTS: Using random forests and multilayer perceptrons, the classifiers achieve average test accuracies of 84-87%, 96.1%, 86%, and 91% respectively. All five fluorophore abundances vary between tumor margin types and tumor grades (p < 0.01). For tissue type, at least four of the five fluorophore abundances are significantly different (p < 0.01) between all classes. CONCLUSIONS: These results demonstrate the fluorophores' differing abundances in different tissue classes and the value of the five fluorophores as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery.

Complete surgical removal of some primary brain tumors is difficult because it can be hard to distinguish the edge of the tumor. We evaluated whether the edges of tumors and the tumor type and grade can be more accurately determined if the tumor is imaged using many different wavelengths of light. We used measurements taken from the tumors of people undergoing brain tumor surgery and developed machine-learning algorithms that could predict where the edge of the tumor was. The methods could also provide information about the type and grade of the brain tumor. These classifications could potentially be used during operations to remove brain tumors more accurately and thus improve the outcome of surgery for people with brain tumors.

3D Global Time-Delay Estimation for Shear-Wave Absolute Vibro-Elastography of the Placenta.

The placenta is a vital organ for growth and development of the fetus. Shear Wave Absolute Vibro-Elastography (SWAVE) is a new elastography technique proposed to detect placenta disorders. Elastography involves applying a force on the tissue and measuring the resulting tissue deformation. All types of compression cause the tissue to expand in three directions given the biological tissues are nearly incompressible. Hence, 3D displacement estimation should lead to the most accurate elasticity reconstruction compared to the traditional 1D methods. Previous studies estimated 3D displacements over ultrasound volumes mostly for quasi-static compression to generate strain images. However, accurate displacement tracking of dynamic motion continues to be a challenge. In this work, a novel volumetric regularized algorithm, 3D GLobal Ultrasound Elastography (GLUE3D), is presented to estimate the 3D displacement over a volume of ultrasound data, following by a 3D Young's modulus reconstruction. The proposed method outperforms the previous 2D method over a volume and is compared with a 3D technique using phantom data for which the elasticity are provided by the values from magnetic resonance elastography on the same phantom and also the manufacturer reference numbers. We then present Young's modulus reconstruction results obtained from clinical data of placenta which shows more uniform elasticity maps compared to the traditional 1D displacement measurements over a volume of ultrasound data. Furthermore, the dependency of the elasticity values to the frequency is investigated in this study.

Detection of an invisible needle in ultrasound using a probabilistic SVM and time-domain features.

We propose a novel learning-based approach to detect an imperceptible hand-held needle in ultrasound images using the natural tremor motion. The minute tremor induced on the needle however is also transferred to the tissue in contact with the needle, making the accurate needle detection a challenging task. The proposed learning-based framework is based on temporal analysis of the phase variations of pixels to classify them according to the motion characteristics. In addition to the classification, we also obtain a probability map of the segmented pixels by cross-validation. A Hough transform is then used on the probability map to localize the needle using the segmented needle and posterior probability estimate. The two-step probability-weighted localization on the segmented needle in a learning framework is the key innovation which results in localization improvement and adaptability to specific clinical applications. The method was tested in vivo for a standard 17 gauge needle inserted at 50-80° insertion angles and 40-60mm depths. The results showed an average accuracy of (2.12°, 1.69mm) and 81%±4% for localization and classification, respectively.

Needle Trajectory and Tip Localization in Real-Time 3-D Ultrasound Using a Moving Stylus.

Described here is a novel approach to needle localization in 3-D ultrasound based on automatic detection of small changes in appearance on movement of the needle stylus. By stylus oscillation, including its full insertion into the cannula to the tip, the image processing techniques can localize the needle trajectory and the tip in the 3-D ultrasound volume. The 3-D needle localization task is reduced to two 2-D localizations using orthogonal projections. To evaluate our method, we tested it on three different ex vivo tissue types, and the preliminary results indicated that the method accuracy lies within clinical acceptance, with average error ranges of 0.9°-1.4° in needle trajectory and 0.8-1.1 mm in needle tip. Results also indicate that method performance is independent of the echogenicity of the tissue. This technique is a safe way of producing ultrasonic intensity changes and appears to introduce negligible risk to the patient, as the outer cannula remains fixed.

AAPM and GEC-ESTRO guidelines for image-guided robotic brachytherapy: report of Task Group 192.

In the last decade, there have been significant developments into integration of robots and automation tools with brachytherapy delivery systems. These systems aim to improve the current paradigm by executing higher precision and accuracy in seed placement, improving calculation of optimal seed locations, minimizing surgical trauma, and reducing radiation exposure to medical staff. Most of the applications of this technology have been in the implantation of seeds in patients with early-stage prostate cancer. Nevertheless, the techniques apply to any clinical site where interstitial brachytherapy is appropriate. In consideration of the rapid developments in this area, the American Association of Physicists in Medicine (AAPM) commissioned Task Group 192 to review the state-of-the-art in the field of robotic interstitial brachytherapy. This is a joint Task Group with the Groupe Européen de Curiethérapie-European Society for Radiotherapy & Oncology (GEC-ESTRO). All developed and reported robotic brachytherapy systems were reviewed. Commissioning and quality assurance procedures for the safe and consistent use of these systems are also provided. Manual seed placement techniques with a rigid template have an estimated in vivo accuracy of 3-6 mm. In addition to the placement accuracy, factors such as tissue deformation, needle deviation, and edema may result in a delivered dose distribution that differs from the preimplant or intraoperative plan. However, real-time needle tracking and seed identification for dynamic updating of dosimetry may improve the quality of seed implantation. The AAPM and GEC-ESTRO recommend that robotic systems should demonstrate a spatial accuracy of seed placement ≤1.0 mm in a phantom. This recommendation is based on the current performance of existing robotic brachytherapy systems and propagation of uncertainties. During clinical commissioning, tests should be conducted to ensure that this level of accuracy is achieved. These tests should mimic the real operating procedure as closely as possible. Additional recommendations on robotic brachytherapy systems include display of the operational state; capability of manual override; documented policies for independent check and data verification; intuitive interface displaying the implantation plan and visualization of needle positions and seed locations relative to the target anatomy; needle insertion in a sequential order; robot-clinician and robot-patient interactions robustness, reliability, and safety while delivering the correct dose at the correct site for the correct patient; avoidance of excessive force on radioactive sources; delivery confirmation of the required number or position of seeds; incorporation of a collision avoidance system; system cleaning, decontamination, and sterilization procedures. These recommendations are applicable to end users and manufacturers of robotic brachytherapy systems.