The impact of computed high b-value images on the diagnostic accuracy of DWI for prostate cancer: A receiver operating characteristics analysis.

To evaluate the performance of computed high b value diffusion-weighted images (DWI) in prostate cancer detection. 97 consecutive patients who had undergone multiparametric MRI of the prostate followed by biopsy were reviewed. Five radiologists independently scored 138 lesions on native high b-value images (b = 1200 s/mm2), apparent diffusion coefficient (ADC) maps, and computed high b-value images (contrast equivalent to b = 2000 s/mm2) to compare their diagnostic accuracy. Receiver operating characteristic (ROC) analysis and McNemar's test were performed to assess the relative performance of computed high b value DWI, native high b-value DWI and ADC maps. No significant difference existed in the area under the curve (AUC) for ROCs comparing B1200 (b = 1200 s/mm2) to computed B2000 (c-B2000) in 5 readers. In 4 of 5 readers c-B2000 had significantly increased sensitivity and/or decreased specificity compared to B1200 (McNemar's p < 0.05), at selected thresholds of interpretation. ADC maps were less accurate than B1200 or c-B2000 for 2 of 5 readers (P < 0.05). This study detected no consistent improvement in overall diagnostic accuracy using c-B2000, compared with B1200 images. Readers detected more cancer with c-B2000 images (increased sensitivity) but also more false positive findings (decreased specificity)

Fibered Confocal Microscopy of Bladder Tumors: An ex Vivo Study

Background and Purpose: The inadequacy of white-light cystoscopy to detect flat bladder tumors is well recognized. Great interest exists in developing other imaging technologies to augment or supplant conventional cystoscopy. Fibered confocal microscopy offers the promise of providing in vivo histopathologic information to help distinguish malignant from benign bladder lesions. We report the initial use of this technology to visualize tumors in the human bladder. Materials and Methods: We performed ex vivo fibered confocal imaging of fresh radical cystectomy specimens using the Mauna Kea Technologies Cellvizio system. The findings were compared with results from standard histopathology. Results: The bladders of four patients were imaged using the fibered confocal microscope. Normal and neoplastic urothelium manifested differences in cellular and vascular density. Conclusion: This study demonstrates the feasibility of using fibered confocal microscopy to detect histologic differences between normal and neoplastic urothelium, and establishes a foundation for the use of fiber-based confocal microscopy in clinical studies.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78112/1/end.2008.0524.pd

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments

Image quality assessment for machine learning tasks using meta-reinforcement learning

In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor provides an objective estimate of task amenability. In this work, we use an IQA controller to predict the task amenability which, itself being parameterised by neural networks, can be trained simultaneously with the task predictor. We further develop a meta-reinforcement learning framework to improve the adaptability for both IQA controllers and task predictors, such that they can be fine-tuned efficiently on new datasets or meta-tasks. We demonstrate the efficacy of the proposed task-specific, adaptable IQA approach, using two clinical applications for ultrasound-guided prostate intervention and pneumonia detection on X-ray images

Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation

Image quality assessment (IQA) in medical imaging can be used to ensure that downstream clinical tasks can be reliably performed. Quantifying the impact of an image on the specific target tasks, also named as task amenability, is needed. A task-specific IQA has recently been proposed to learn an image-amenability-predicting controller simultaneously with a target task predictor. This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e.g. segmentation and classification neural networks in modern clinical applications. In this work, we propose an extension to this task-specific IQA approach, by adding a task-agnostic IQA based on auto-encoding as the target task. Analysing the intersection between low-quality images, deemed by both the task-specific and task-agnostic IQA, may help to differentiate the underpinning factors that caused the poor target task performance. For example, common imaging artefacts may not adversely affect the target task, which would lead to a low task-agnostic quality and a high task-specific quality, whilst individual cases considered clinically challenging, which can not be improved by better imaging equipment or protocols, is likely to result in a high task-agnostic quality but a low task-specific quality. We first describe a flexible reward shaping strategy which allows for the adjustment of weighting between task-agnostic and task-specific quality scoring. Furthermore, we evaluate the proposed algorithm using a clinically challenging target task of prostate tumour segmentation on multiparametric magnetic resonance (mpMR) images, from 850 patients. The proposed reward shaping strategy, with appropriately weighted task-specific and task-agnostic qualities, successfully identified samples that need re-acquisition due to defected imaging process.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.or

Diagnosis of prostate cancer by desorption electrospray ionization mass spectrometric imaging of small metabolites and lipids

Accurate identification of prostate cancer in frozen sections at the time of surgery can be challenging, limiting the surgeon's ability to best determine resection margins during prostatectomy. We performed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) on 54 banked human cancerous and normal prostate tissue specimens to investigate the spatial distribution of a wide variety of small metabolites, carbohydrates, and lipids. In contrast to several previous studies, our method included Krebs cycle intermediates (m/z <200), which we found to be highly informative in distinguishing cancer from benign tissue. Malignant prostate cells showed marked metabolic derangements compared with their benign counterparts. Using the "Least absolute shrinkage and selection operator" (Lasso), we analyzed all metabolites from the DESI-MS data and identified parsimonious sets of metabolic profiles for distinguishing between cancer and normal tissue. In an independent set of samples, we could use these models to classify prostate cancer from benign specimens with nearly 90% accuracy per patient. Based on previous work in prostate cancer showing that glucose levels are high while citrate is low, we found that measurement of the glucose/citrate ion signal ratio accurately predicted cancer when this ratio exceeds 1.0 and normal prostate when the ratio is less than 0.5. After brief tissue preparation, the glucose/citrate ratio can be recorded on a tissue sample in 1 min or less, which is in sharp contrast to the 20 min or more required by histopathological examination of frozen tissue specimens. prostate cancer | Krebs cycle | metabolism | desorption electrospray ionization | mass spectrometry P rostate cancer (PCa) is the most commonly diagnosed solidorgan cancer and the second leading cause of cancer death in men in the United States (1). Because of prostate-specific antigen (PSA) screening in the United States, most PCas are discovered when they are confined to the prostate (2). Many of these localized PCas are treated by surgical removal of the entire prostate (radical prostatectomy). The presence of cancer cells at the edge of the surgical resection, or positive surgical margins, is associated with higher rates of recurrence and death from PCa (3, 4). Therefore, an important clinical challenge in PCa management is to devise a rapid and highly accurate method to detect cancerous cells in real time to allow resection of additional periprostatic tissues and reduce cancer recurrence after surgery. Over the last decade, several innovative analytical techniques (5-12) have been developed to distinguish cancer from benign tissue in various organs. However, none has achieved wide clinical adoption for various reasons including inconvenience, narrow information content, unavailability, poor sensitivity, slowness of adoption, and operating room workflow incompatibility. In PCa, intraoperative frozen sections have been used to attempt to identify PCa at the margin based on analysis of histology. However, frozen sections have been shown to have poor sensitivity and specificity for the detection of PCa and currently are not recommended Recently, a label-free molecular imaging method called desorption electrospray ionization mass spectrometric imaging (DESI-MSI) has been developed (15-17). DESI-MSI can rapidly evaluate the tissue metabolome in situ by simultaneously characterizing hundreds of lipids and metabolites. In the last 5 y, reports from our group Given the known alterations in metabolic pathways in PCa, we tested whether DESI-MSI of metabolites and lipids could have utility in discriminating cancer from normal tissue obtained from radical prostatectomy. Using tandem and high-resolution mass spectrometry we have characterized the distinct metabolite and lipid profiles of normal and malignant prostate. Although many earlier DESI-MSI studies considered only lipid profiles in identification of cancer, here we report imaging small metabolite Significance Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is a label-free molecular imaging technique that provides a window into the biochemical processes present in benign and malignant prostate tissue. This is important both in improving the understanding of tissue biology and in achieving rapid cancer diagnosis. We applied DESI-MSI to record lipid, carbohydrate, and most importantly, small metabolite images from 54 normal and malignant prostate tissue specimens. Several Krebs cycle intermediates were present at different concentrations in prostate cancer compared with normal tissue. Statistical calculations identified panels of metabolites that could readily distinguish prostate cancer from normal tissue with nearly 90% accuracy in a validation set. The results also indicated that the ratio of glucose to citrate ion signals could be used to accurately identify prostate cancer

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods