Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that 1) jointly optimising a third conditioning neural network that pre-processes the input image, can effectively extract patient-specific features for conditioning; and 2) combining multiple generative models trained separately with heuristically pre-disjointed training data sets can adequately mitigate the problem of mode collapse. Trained with diagnostic T2-weighted MR images from 143 real patients and 73,216 3D dense displacement fields from finite element simulations of intraoperative prostate motion due to transrectal ultrasound probe pressure, the proposed models produced physically-plausible patient-specific motion of prostate glands. The ability to capture biomechanically simulated motion was evaluated using two errors representing generalisability and specificity of the model. The median values, calculated from a 10-fold cross-validation, were 2.8+/-0.3 mm and 1.7+/-0.1 mm, respectively. We conclude that the introduced approach demonstrates the feasibility of applying state-of-the-art machine learning algorithms to generate organ motion models from patient images, and shows significant promise for future research.Comment: Accepted to MICCAI 201

An analysis of the Research Fellowship Scheme of the Royal College of Surgeons of England.

BACKGROUND: The Research Fellowship Scheme of the Royal College of Surgeons of England commenced in 1993 with the aim of exposing selected surgical trainees to research techniques and methodology, with the hope of having an impact on surgical research and increasing the cadre of young surgeons who might decide to pursue an academic career in surgery. Over 11 million pounds sterling (approximately US 20 million dollars) has been invested in 264 fellowships. The College wished to evaluate the impact of the Scheme on the careers of research fellows, surgical research, and patient care. As the 10th anniversary of the Scheme approached. STUDY DESIGN: Two-hundred and sixty research fellows whose current addresses were available were sent a questionnaire. Two-hundred and thirty-eight (91.5%) responded. RESULTS: Three-quarters of the research fellows conducted laboratory-based research, with most of the remainder conducting patient-based clinical research. One-third of the fellows who have reached consultant status have an academic component to their post. The total number of publications based on fellowship projects was 531, with a median impact factor of 3.5. Almost all fellows had been awarded a higher degree or were working toward this. Half of the fellows received subsequent funding for research, mostly awarded by national or international funding bodies. CONCLUSIONS: The Research Fellowship Scheme of the Royal College of Surgeons of England has successfully supported many trainee surgeons in the initial phase of their research career. It has helped surgical research by increasing the pool of surgeons willing to embark on an academic career. Indirectly, patient care has benefited by promoting an evidence-based culture among young surgeons. Such schemes are relevant to surgical training programs elsewhere if more young surgeons are to be attracted into academic surgery

Adversarial Deformation Regularization for Training Image Registration Neural Networks

We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration-predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adversarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.Comment: Accepted to MICCAI 201

Deep Learning-Based Long Term Mortality Prediction in the National Lung Screening Trial

In this study, the long-term mortality in the National Lung Screening Trial (NLST) was investigated using a deep learning-based method. Binary classification of the non-lung-cancer mortality (i.e. cardiovascular and respiratory mortality) was performed using neural network models centered around a 3D-ResNet. The models were trained on a participant age, gender, and smoking history matched cohort. Utilising both the 3D CT scan and clinical information, the models can achieve an AUC of 0.73 which outperforms humans at cardiovascular mortality prediction. The corresponding F1 and Matthews Correlation Coefficient are 0.60 and 0.38 respectively. By interpreting the trained models with 3D saliency maps, we examined the features on the CT scans that correspond to the mortality signal. By extracting information from 3D CT volumes, we can highlight regions in the thorax region that contribute to mortality that might be overlooked by the clinicians. Therefore, this can help focus preventative interventions appropriately, particularly for under-recognised pathologies and thereby reducing patient morbidity

Evaluating variation in use of definitive therapy and risk-adjusted prostate cancer mortality in England and the USA.

OBJECTIVES: Prostate cancer mortality (PCM) in the USA is among the lowest in the world, whereas PCM in England is among the highest in Europe. This paper aims to assess the association of variation in use of definitive therapy on risk-adjusted PCM in England as compared with the USA. DESIGN: Observational study. SETTING: Cancer registry data from England and the USA. PARTICIPANTS: Men diagnosed with non-metastatic prostate cancer (PCa) in England and the USA between 2004 and 2008. OUTCOME MEASURES: Competing-risks survival analyses to estimate subhazard ratios (SHR) of PCM adjusted for age, ethnicity, year of diagnosis, Gleason score (GS) and clinical tumour (cT) stage. RESULTS: 222,163 men were eligible for inclusion. Compared with American patients, English patients were more likely to present at an older age (70-79 years: England 44.2%, USA 29.3%, p<0.001), with higher tumour stage (cT3-T4: England 25.1%, USA 8.6%, p<0.001) and higher GS (GS 8-10: England 20.7%, USA 11.2%, p<0.001). They were also less likely to receive definitive therapy (England 38%, USA 77%, p<0.001). English patients were more likely to die of PCa (SHR=1.9, 95% CI 1.7 to 2.0, p<0.001). However, this difference was no longer statistically significant when also adjusted for use of definitive therapy (SHR=1.0, 95% CI 1.0 to 1.1, p=0.3). CONCLUSIONS: Risk-adjusted PCM is significantly higher in England compared with the USA. This difference may be explained by less frequent use of definitive therapy in England

Multiparametric MR imaging for detection of clinically significant prostate cancer: a validation cohort study with transperineal template prostate mapping as the reference standard.

PURPOSE: To evaluate the diagnostic performance of multiparametric (MP) magnetic resonance (MR) imaging for prostate cancer detection by using transperineal template prostate mapping (TTPM) biopsies as the reference standard and to determine the potential ability of MP MR imaging to identify clinically significant prostate cancer. MATERIALS AND METHODS: Institutional review board exemption was granted by the local research ethics committee for this retrospective study. Included were 64 men (mean age, 62 years [range, 40-76]; mean prostate-specific antigen, 8.2 ng/mL [8.2 μg/L] [range, 2.1-43 ng/mL]), 51 with biopsy-proved cancer and 13 suspected of having clinically significant cancer that was biopsy negative or without prior biopsy. MP MR imaging included T2-weighted, dynamic contrast-enhanced and diffusion-weighted imaging (1.5 T, pelvic phased-array coil). Three radiologists independently reviewed images and were blinded to results of biopsy. Two-by-two tables were derived by using sectors of analysis of four quadrants, two lobes, and one whole prostate. Primary target definition for clinically significant disease necessary to be present within a sector of analysis on TTPM for that sector to be deemed positive was set at Gleason score of 3+4 or more and/or cancer core length involvement of 4 mm or more. Sensitivity, negative predictive value, and negative likelihood ratio were calculated to determine ability of MP MR imaging to rule out cancer. Specificity, positive predictive value, positive likelihood ratio, accuracy (overall fraction correct), and area under receiver operating characteristic curves were also calculated. RESULTS: Twenty-eight percent (71 of 256) of sectors had clinically significant cancer by primary endpoint definition. For primary endpoint definition (≥ 4 mm and/or Gleason score ≥ 3+4), sensitivity, negative predictive value, and negative likelihood ratios were 58%-73%, 84%-89%, and 0.3-0.5, respectively. Specificity, positive predictive value, and positive likelihood ratios were 71%-84%, 49%-63%, and 2.-3.44, respectively. Area under the curve values were 0.73-0.84. CONCLUSION: Results of this study indicate that MP MR imaging has a high negative predictive value to rule out clinically significant prostate cancer and may potentially have clinical use in diagnostic pathways of men at risk

Label-driven weakly-supervised learning for multimodal deformable image registration

Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms. We propose a weakly-supervised, label-driven formulation for learning 3D voxel correspondence from higher-level label correspondence, thereby bypassing classical intensity-based image similarity measures. During training, a convolutional neural network is optimised by outputting a dense displacement field (DDF) that warps a set of available anatomical labels from the moving image to match their corresponding counterparts in the fixed image. These label pairs, including solid organs, ducts, vessels, point landmarks and other ad hoc structures, are only required at training time and can be spatially aligned by minimising a cross-entropy function of the warped moving label and the fixed label. During inference, the trained network takes a new image pair to predict an optimal DDF, resulting in a fully-automatic, label-free, real-time and deformable registration. For interventional applications where large global transformation prevails, we also propose a neural network architecture to jointly optimise the global- and local displacements. Experiment results are presented based on cross-validating registrations of 111 pairs of T2-weighted magnetic resonance images and 3D transrectal ultrasound images from prostate cancer patients with a total of over 4000 anatomical labels, yielding a median target registration error of 4.2 mm on landmark centroids and a median Dice of 0.88 on prostate glands.Comment: Accepted to ISBI 201

Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators

We show that DNN accelerator micro-architectures and their program mappings represent specific choices of loop order and hardware parallelism for computing the seven nested loops of DNNs, which enables us to create a formal taxonomy of all existing dense DNN accelerators. Surprisingly, the loop transformations needed to create these hardware variants can be precisely and concisely represented by Halide's scheduling language. By modifying the Halide compiler to generate hardware, we create a system that can fairly compare these prior accelerators. As long as proper loop blocking schemes are used, and the hardware can support mapping replicated loops, many different hardware dataflows yield similar energy efficiency with good performance. This is because the loop blocking can ensure that most data references stay on-chip with good locality and the processing units have high resource utilization. How resources are allocated, especially in the memory system, has a large impact on energy and performance. By optimizing hardware resource allocation while keeping throughput constant, we achieve up to 4.2X energy improvement for Convolutional Neural Networks (CNNs), 1.6X and 1.8X improvement for Long Short-Term Memories (LSTMs) and multi-layer perceptrons (MLPs), respectively.Comment: Published as a conference paper at ASPLOS 202