Physics-Informed Computer Vision: A Review and Perspectives

Incorporation of physical information in machine learning frameworks are opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work we explore their utility for computer vision tasks in interpreting and understanding visual data. We present a systematic literature review of formulation and approaches to computer vision tasks guided by physical laws. We begin by decomposing the popular computer vision pipeline into a taxonomy of stages and investigate approaches to incorporate governing physical equations in each stage. Existing approaches in each task are analyzed with regard to what governing physical processes are modeled, formulated and how they are incorporated, i.e. modify data (observation bias), modify networks (inductive bias), and modify losses (learning bias). The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research. While still in its early days, the study of physics-informed computer vision has the promise to develop better computer vision models that can improve physical plausibility, accuracy, data efficiency and generalization in increasingly realistic applications

Electromagnetic Induction Imaging with Atomic Magnetometers

Electromagnetic induction imaging (EMI) is a technique for non-invasively mapping the passive electromagnetic properties of materials. It involves the active probing of samples with a radio-frequency magnetic field and recording the details of the magnetic field produced by the induced eddy current response. The performance of an EMI system is ultimately determined by the choice of magnetic field sensor used in the measurement. The sensor’s sensitivity, range of operation frequency, and sensing volume are all crucial characteristics when considering the imaging platform’s capabilities. Atomic magnetometers (AMs) – based on the coherent precession of a polarised alkali atomic vapour – currently rate amongst the most sensitive devices for magnetic field measurements. Radio-frequency atomic magnetometers (RF-AMs) are ultra-sensitive detectors of oscillating magnetic fields across a broad range of frequencies. As such, they are ideally suited to EMI applications. This work presents the development of EMI systems based on RF-AMs. The imaging performance and a wide range of applications are experimentally demonstrated. The continuous development of a single-channel rubidium RF-AM is described. The final device operates in unshielded environments and near room temperature with a measured sensitivity of 55 fT/√Hz, a photon-shot noise limit of 10 fT/√Hz, and a linewidth of 36 Hz. Tunability of the device is proven by consistent, narrow-linewidth operation across the kHz – MHz band – operating in magnetic fields significantly greater than previous AM designs. The sensor was developed with a small effective sensor volume, which increases the spatial resolution of the imaging. High-resolution EMI is performed across a broad range of materials. This spans the first EMI images with an RF-AM at 6x107 S/m to low-conductivity, non-metallic samples at 500 S/m. Typically, sample volumes are of a few cm3 and with an imaging resolution around 1 mm. These numbers make EMI with AMs (EMI-AM) suitable for numerous applications. Techniques – including multi-frequency image analysis – are employed to discriminate sample properties. Further work developed novel image reconstruction approaches – based on machine learning – to maximise the amount of information that can be extracted from EMI images. Finally, the potential of biomedical imaging is discussed and its feasibility verified by simulating the application of EMI-AM to imaging the conductivity of the heart

Development of Synchrotron Based Imaging Tools for Benign Prostatic Hyperplasia Using an Induced Canine Model

Benign prostatic hyperplasia (BPH) is a disease that develops spontaneously in men and dogs, and the dog is an accepted model to study BPH in men. As the gland can also be the site of a malignant cancer, definitive diagnosis relies on histological evaluation of prostate tissue following an invasive biopsy. The use of a new imaging technique, synchrotron-based phase contrast computed tomography (PC-CT) for excised prostates shows great detail of the internal structure of the gland, comparable to that of low power histology. Considering this fact, our main objective was to induce BPH in dogs using a combination of hormones dihydrotestosterone (DHT) and estradiol and verify if PC-CT imaging of in situ prostate glands of live dogs allowed for improved non-invasive imaging details compared to conventional medical imaging modalities and early diagnosis of BPH. Two studies were conducted to achieve the objectives. The first study involved the induction of BPH using intact male dogs with DHT and estradiol. The control group (n=3) received triolein carrier and the BPH induction group (n=3) received the hormone combination of DHT and estradiol (dissolved in triolein) injections three times/week for 35 weeks. Parameters that were assessed to diagnosis BPH were in vivo prostate volumes calculated using computed tomography (CT) images, volume of excised prostates determined using water displacement, 3D measurements of the excised gland, semen analysis, digital rectal exam (DRE), hormone analysis (estradiol, testosterone, DHT and canine prostate specific esterase CPSE), morphometric analysis and histological assessment of the tissue slides. The results showed that the gland volumes calculated both in vivo and ex vivo and the 3D measurements from BPH induction group were numerically higher than the control group. Sperm count decreased for all dogs but was reduced to zero or almost zero in the BPH induction group. The CPSE hormone analysis indicated that 1 dog from the treatment and 1 dog from the control group had BPH according to the manufacture’s defined threshold value. DHT hormone levels were higher for the induction group than controls throughout the entire study. This consequently affected the endogenous testosterone and estradiol, which were both decreased due to the negative feedback in the hypothalamic-pituitary-gonadal (HPG) axis. For DRE, prostates from all dogs were found to be enlarged in size by the end of the study and the histological diagnosis revealed that all dogs have a certain degree of BPH. The second study involved the imaging of all six dogs with conventional non-invasive modalities (magnetic resonance imaging, MRI; CT; positron emission tomography, PET-CT; ultrasound, US; radiographs) plus the innovative PC-CT technique at the Canadian Light Source (CLS). None of these techniques resulted in images with the same level of fine detail as that obtained with previous PC-CT imaging of excised canine prostates. Comparisons among images from the various modalities determined that the best modality for the visualization of the internal structures of the prostate gland such as capsule, parenchyma, septa, lobe, urethra and cysts was MRI (T2), followed by US and CT. PC-CT images were comparable with PET-CT, allowing the visualization of the lobes and urethra filled with tracer. In conclusion, all dogs developed BPH, either spontaneously (control group) or following induction (treatment group). Also, images of the in situ prostate gland of dogs were acquired for the first time at the CLS with the PC-CT technique. Although the quality and resolution was not as expected in comparison with PC-CT images of excised canine prostates, this technique shows promise and with additional study and development has the potential to become a useful diagnostic methodology