Summary Background Poly(ADP-ribose) polymerase (PARP) inhibitors have activity in ovarian carcinomas with homologous recombination deficiency. Along with BRCA1 and BRCA2 (BRCA) mutations genomic loss of heterozygosity (LOH) might also represent homologous recombination deficiency. In ARIEL2, we assessed the ability of tumour genomic LOH, quantified with a next-generation sequencing assay, to predict response to rucaparib, an oral PARP inhibitor. Methods ARIEL2 is an international, multicentre, two-part, phase 2, open-label study done at 49 hospitals and cancer centres in Australia, Canada, France, Spain, the UK, and the USA. In ARIEL2 Part 1, patients with recurrent, platinum-sensitive, high-grade ovarian carcinoma were classified into one of three predefined homologous recombination deficiency subgroups on the basis of tumour mutational analysis: BRCA mutant (deleterious germline or somatic), BRCA wild-type and LOH high (LOH high group), or BRCA wild-type and LOH low (LOH low group). We prespecified a cutoff of 14% or more genomic LOH for LOH high. Patients began treatment with oral rucaparib at 600 mg twice per day for continuous 28 day cycles until disease progression or any other reason for discontinuation. The primary endpoint was progression-free survival. All patients treated with at least one dose of rucaparib were included in the safety analyses and all treated patients who were classified were included in the primary endpoint analysis. This trial is registered with ClinicalTrials.gov, number NCT01891344. Enrolment into ARIEL2 Part 1 is complete, although an extension (Part 2) is ongoing. Findings 256 patients were screened and 206 were enrolled between Oct 30, 2013, and Dec 19, 2014. At the data cutoff date (Jan 18, 2016), 204 patients had received rucaparib, with 28 patients remaining in the study. 192 patients could be classified into one of the three predefined homologous recombination deficiency subgroups: BRCA mutant (n=40), LOH high (n=82), or LOH low (n=70). Tumours from 12 patients were established as BRCA wild-type, but could not be classified for LOH, because of insufficient neoplastic nuclei in the sample. The median duration of treatment for the 204 patients was 5·7 months (IQR 2·8–10·1). 24 patients in the BRCA mutant subgroup, 56 patients in the LOH high subgroup, and 59 patients in the LOH low subgroup had disease progression or died. Median progression-free survival after rucaparib treatment was 12·8 months (95% CI 9·0–14·7) in the BRCA mutant subgroup, 5·7 months (5·3–7·6) in the LOH high subgroup, and 5·2 months (3·6–5·5) in the LOH low subgroup. Progression-free survival was significantly longer in the BRCA mutant (hazard ratio 0·27, 95% CI 0·16–0·44, p Interpretation In patients with BRCA mutant or BRCA wild-type and LOH high platinum-sensitive ovarian carcinomas treated with rucaparib, progression-free survival was longer than in patients with BRCA wild-type LOH low carcinomas. Our results suggest that assessment of tumour LOH can be used to identify patients with BRCA wild-type platinum-sensitive ovarian cancers who might benefit from rucaparib. These results extend the potential usefulness of PARP inhibitors in the treatment setting beyond BRCA mutant tumours. Funding Clovis Oncology, US Department of Defense Ovarian Cancer Research Program, Stand Up To Cancer—Ovarian Cancer Research Fund Alliance—National Ovarian Cancer Coalition Dream Team Translational Research Grant, and V Foundation Translational Award.

https://escholarship.org/content/qt1gv7r4vj/qt1gv7r4vj.pdf?t=oht4fn

Rucaparib in relapsed, platinum-sensitive high-grade ovarian carcinoma (ARIEL2 Part 1): an international, multicentre, open-label, phase 2 trial

Visual metrics can play an important role in the evaluation of novel lighting, rendering, and imaging algorithms. Unfortunately, current metrics only work well for narrow intensity ranges, and do not correlate well with experimental data outside these ranges. To address these issues, we propose a visual metric for predicting visibility (discrimination) and quality (mean-opinion-score). The metric is based on a new visual model for all luminance conditions, which has been derived from new contrast sensitivity measurements. The model is calibrated and validated against several contrast discrimination data sets, and image quality databases (LIVE and TID2008). The visibility metric is shown to provide much improved predictions as compared to the original HDR-VDP and VDP metrics, especially for low luminance conditions. The image quality predictions are comparable to or better than for the MS-SSIM, which is considered one of the most successful quality metrics. The code of the proposed metric is available on-line.

HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions

Demosaicking and denoising are the key first stages of the digital imaging pipeline but they are also a severely ill-posed problem that infers three color values per pixel from a single noisy measurement. Earlier methods rely on hand-crafted filters or priors and still exhibit disturbing visual artifacts in hard cases such as moire or thin edges. We introduce a new data-driven approach for these challenges: we train a deep neural network on a large corpus of images instead of using hand-tuned filters. While deep learning has shown great success, its naive application using existing training datasets does not give satisfactory results for our problem because these datasets lack hard cases. To create a better training set, we present metrics to identify difficult patches and techniques for mining community photographs for such patches. Our experiments show that this network and training procedure outperform state-of-the-art both on noisy and noise-free data. Furthermore, our algorithm is an order of magnitude faster than the previous best performing techniques.

Deep joint demosaicking and denoising

We propose a tone mapping operator that can minimize visible contrast distortions for a range of output devices, ranging from e-paper to HDR displays. The operator weights contrast distortions according to their visibility predicted by the model of the human visual system. The distortions are minimized given a display model that enforces constraints on the solution. We show that the problem can be solved very efficiently by employing higher order image statistics and quadratic programming. Our tone mapping technique can adjust image or video content for optimum contrast visibility taking into account ambient illumination and display characteristics. We discuss the differences between our method and previous approaches to the tone mapping problem.

/pdf/display-adaptive-tone-mapping-2bmjyq68e0.pdf

Display adaptive tone mapping

Producing a high dynamic range (HDR) image from a set of images with different exposures is a challenging process for dynamic scenes. A category of existing techniques first register the input images to a reference image and then merge the aligned images into an HDR image. However, the artifacts of the registration usually appear as ghosting and tearing in the final HDR images. In this paper, we propose a learning-based approach to address this problem for dynamic scenes. We use a convolutional neural network (CNN) as our learning model and present and compare three different system architectures to model the HDR merge process. Furthermore, we create a large dataset of input LDR images and their corresponding ground truth HDR images to train our system. We demonstrate the performance of our system by producing high-quality HDR images from a set of three LDR images. Experimental results show that our method consistently produces better results than several state-of-the-art approaches on challenging scenes.

Deep high dynamic range imaging of dynamic scenes

New generations of display devices promise to provide significantly improved dynamic range over conventional display technology. In the long run, evolving camera technology and file formats will provide high fidelity content for these display devices. In the near term, however, the vast majority of images and video will only be available in low dynamic range formats. In this paper we describe a method for boosting the dynamic range of legacy video and photographs for viewing on high dynamic range displays. Our emphasis is on real-time processing of video streams, such as web streams or the signal from a DVD player. We place particular emphasis on robustness of the method, and its ability to deal with a wide range of content without user adjusted parameters or visible artifacts. The method can be implemented on both graphics hardware and on signal processors that are directly integrated in the HDR displays.

Ldr2Hdr: on-the-fly reverse tone mapping of legacy video and photographs

https://www.gynecologiconcology-online.net/article/S0090-8258(08)00370-3/pdf

Chromosomal instability in fallopian tube precursor lesions of serous carcinoma and frequent monoclonality of synchronous ovarian and fallopian tube mucosal serous carcinoma

Methods and apparatus according to various aspects take as input image data in a lower-dynamic-range (LDR) format and produce as output enhanced image data having a dynamic range greater than that of the input image data (i.e. higher-dynamic range (HDR) image data). In some embodiments, the methods are applied to video data and are performed in real-time (i.e. processing of video frames to enhance the dynamic range of the video frames is completed at least on average at the frame rate of the video signal).

Enhancing dynamic ranges of images

Recent high dynamic range (HDR) display devices provide significantly greater output brightness and dynamic range compared to conventional display technology. A possible concern for the extended use of HDR displays is the potential to cause visual fatigue. Furthermore, ambient illumination has a significant effect on the perception of the imagery displayed, and its impact on user preferences for brightness and contrast must be understood.In our work we examine these issues by conducting two user studies. In each study, subjects watched video content on an HDR display in several different ambient illumination environments, and were asked to adjust the brightness and black level of the display to their preference. Subjects were also given questionnaires to document their observations and subjective preferences as well as any visual fatigue they may have experienced. We found that subjects experienced minimal visual fatigue, and also found statistically significant differences in preferred display settings under different ambient lighting conditions.

/pdf/video-viewing-preferences-for-hdr-displays-under-varying-2vyz0ghb29.pdf

Allan G. Rempel

Papers

HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions

Ldr2Hdr: on-the-fly reverse tone mapping of legacy video and photographs

Chromosomal instability in fallopian tube precursor lesions of serous carcinoma and frequent monoclonality of synchronous ovarian and fallopian tube mucosal serous carcinoma

Enhancing dynamic ranges of images

Video viewing preferences for HDR displays under varying ambient illumination