Exclusion of non-Involved uterus from the target volume (EXIT-trial): An individualized treatment for locally advanced cervical cancer using modern radiotherapy and imaging techniques followed by completion surgery.

Background and purpose: Chemoradiotherapy followed by brachytherapy is the standard of care for locally advanced cervical cancer (LACC). In this study, we postulate that omitting an iconographical unaffected uterus (+12 mm distance from the tumour) from the treatment volume is safe and that no tumour will be found in the non-targeted uterus (NTU) leading to reduction of high-dose volumes of surrounding organs at risk (OARs). Material and Methods: In this single-arm phase 2 study, two sets of target volumes were delineated: one standard-volume (whole uterus) and an EXIT-volume (exclusion of non-tumour-bearing parts of the uterus with a minimum 12 mm margin from the tumour). All patients underwent chemoradiotherapy targeting the EXIT-volume, followed by completion hysterectomy. In 15 patients, a plan comparison between two treatment plans (PTV vs PTV_EXIT) was performed. The primary endpoint was the pathological absence of tumour involvement in the non-targeted uterus (NTU). Secondary endpoints included dosimetric impact of target volume reduction on OARs, acute and chronic toxicity, overall survival (OS), locoregional recurrence-free survival (LRFS), and progression-free survival (PFS). Results: In all 21 (FIGO stage I: 2; II: 14;III: 3; IV: 2) patients the NTU was pathologically negative. Ssignificant reductions in Dmean in bladder, sigmoid and rectum; V15Gy in sigmoid and rectum, V30Gy in bladder, sigmoid and rectum; V40Gy and V45Gy in bladder, bowel bag, sigmoid and rectum; V50Gy in rectum were achieved. Median follow-up was 54 months (range 7-79 months). Acute toxicity was mainly grade 2 and 5 % grade 3 urinary. The 3y- OS, PFS and LRFS were respectively 76,2%, 64,9% and 81 %. Conclusion: MRI-based exclusion of the non-tumour-bearing parts of the uterus at a minimum distance of 12 mm from the tumour out of the target volume in LACC can be done without risk of residual disease in the NTU, leading to a significant reduction of the volume of surrounding OARS treated to high doses.

Deep learning for automatic segmentation of vestibular schwannoma: a retrospective study from multi-center routine MRI.

Automatic segmentation of vestibular schwannoma (VS) from routine clinical MRI has potential to improve clinical workflow, facilitate treatment decisions, and assist patient management. Previous work demonstrated reliable automatic segmentation performance on datasets of standardized MRI images acquired for stereotactic surgery planning. However, diagnostic clinical datasets are generally more diverse and pose a larger challenge to automatic segmentation algorithms, especially when post-operative images are included. In this work, we show for the first time that automatic segmentation of VS on routine MRI datasets is also possible with high accuracy. We acquired and publicly release a curated multi-center routine clinical (MC-RC) dataset of 160 patients with a single sporadic VS. For each patient up to three longitudinal MRI exams with contrast-enhanced T1-weighted (ceT1w) (n = 124) and T2-weighted (T2w) (n = 363) images were included and the VS manually annotated. Segmentations were produced and verified in an iterative process: (1) initial segmentations by a specialized company; (2) review by one of three trained radiologists; and (3) validation by an expert team. Inter- and intra-observer reliability experiments were performed on a subset of the dataset. A state-of-the-art deep learning framework was used to train segmentation models for VS. Model performance was evaluated on a MC-RC hold-out testing set, another public VS datasets, and a partially public dataset. The generalizability and robustness of the VS deep learning segmentation models increased significantly when trained on the MC-RC dataset. Dice similarity coefficients (DSC) achieved by our model are comparable to those achieved by trained radiologists in the inter-observer experiment. On the MC-RC testing set, median DSCs were 86.2(9.5) for ceT1w, 89.4(7.0) for T2w, and 86.4(8.6) for combined ceT1w+T2w input images. On another public dataset acquired for Gamma Knife stereotactic radiosurgery our model achieved median DSCs of 95.3(2.9), 92.8(3.8), and 95.5(3.3), respectively. In contrast, models trained on the Gamma Knife dataset did not generalize well as illustrated by significant underperformance on the MC-RC routine MRI dataset, highlighting the importance of data variability in the development of robust VS segmentation models. The MC-RC dataset and all trained deep learning models were made available online.

Enhancing surgical instrument segmentation: integrating vision transformer insights with adapter.

PURPOSE: In surgical image segmentation, a major challenge is the extensive time and resources required to gather large-scale annotated datasets. Given the scarcity of annotated data in this field, our work aims to develop a model that achieves competitive performance with training on limited datasets, while also enhancing model robustness in various surgical scenarios. METHODS: We propose a method that harnesses the strengths of pre-trained Vision Transformers (ViTs) and data efficiency of convolutional neural networks (CNNs). Specifically, we demonstrate how a CNN segmentation model can be used as a lightweight adapter for a frozen ViT feature encoder. Our novel feature adapter uses cross-attention modules that merge the multiscale features derived from the CNN encoder with feature embeddings from ViT, ensuring integration of the global insights from ViT along with local information from CNN. RESULTS: Extensive experiments demonstrate our method outperforms current models in surgical instrument segmentation. Specifically, it achieves superior performance in binary segmentation on the Robust-MIS 2019 dataset, as well as in multiclass segmentation tasks on the EndoVis 2017 and EndoVis 2018 datasets. It also showcases remarkable robustness through cross-dataset validation across these 3 datasets, along with the CholecSeg8k and AutoLaparo datasets. Ablation studies based on the datasets prove the efficacy of our novel adapter module. CONCLUSION: In this study, we presented a novel approach integrating ViT and CNN. Our unique feature adapter successfully combines the global insights of ViT with the local, multi-scale spatial capabilities of CNN. This integration effectively overcomes data limitations in surgical instrument segmentation. The source code is available at: https://github.com/weimengmeng1999/AdapterSIS.git .

Disease Control and Late Toxicity in Adaptive Dose Painting by Numbers Versus Nonadaptive Radiation Therapy for Head and Neck Cancer: A Randomized Controlled Phase 2 Trial.

PURPOSE: Local recurrence remains the main cause of death in stage III-IV nonmetastatic head and neck cancer (HNC), with relapse-prone regions within high 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET)-signal gross tumor volume. We investigated if dose escalation within this subvolume combined with a 3-phase treatment adaptation could increase local (LC) and regional (RC) control at equal or minimized radiation-induced toxicity, by comparing adaptive 18F-FDG-PET voxel intensity-based dose painting by numbers (A-DPBN) with nonadaptive standard intensity modulated radiation therapy (S-IMRT). METHODS AND MATERIALS: This 2-center randomized controlled phase 2 trial assigned (1:1) patients to receive A-DPBN or S-IMRT (+/-chemotherapy). Eligibility: nonmetastatic HNC of oral cavity, oro-/hypopharynx, or larynx, needing radio(chemo)therapy; T1-4N0-3 (exception: T1-2N0 glottic); KPS ≥ 70; ≥18 years; and informed consent. PRIMARY OUTCOMES: 1-year LC and RC. The dose prescription for A-DPBN was intercurrently adapted in 2 steps to an absolute dose-volume limit (≤1.75 cm3 can receive >84 Gy and normalized isoeffective dose >96 Gy) as a safety measure during the study course after 4/7 A-DPBN patients developed ≥G3 mucosal ulcers. RESULTS: Ninety-five patients were randomized (A-DPBN, 47; S-IMRT, 48). Median follow-up was 31 months (IQR, 14-48 months); 29 patients died (17 of cancer progression). A-DPBN resulted in superior LC compared with S-IMRT, with 1- and 2-year LC of 91% and 88% versus 78% and 75%, respectively (hazard ratio, 3.13; 95% CI, 1.13-8.71; P = .021). RC and overall survival were comparable between arms, as was overall grade (G) ≥3 late toxicity (36% vs 20%; P = .1). More ≥G3 late mucosal ulcers were observed in active smokers (29% vs 3%; P = .005) and alcohol users (33% vs 13%; P = .02), independent of treatment arm. Similarly, in the A-DPBN arm, significantly more patients who smoked at diagnosis developed ≥G3 (46% vs 12%; P = .005) and ≥G4 (29% vs 8%; P = .048) mucosal ulcers. One arterial blowout occurred after a G5 mucosal toxicity. CONCLUSIONS: A-DPBN resulted in superior 1- and 2-year LC for HNC compared with S-IMRT. This supports further exploration in multicenter phase 3 trials. It will, however, be challenging to recruit a substantial patient sample for such trials, as concerns have arisen regarding the association of late mucosal ulcers when escalating the dose in continuing smokers.

High-fidelity, low-cost synthetic training model for fetoscopic spina bifida repair.

BACKGROUND: Fetoscopic spina bifida repair is increasingly being practiced, but limited skill acquisition poses a barrier to widespread adoption. Extensive training in relevant models, including both ex vivo and in vivo models may help. To address this, a synthetic training model that is affordable, realistic, and that allows skill analysis would be useful. OBJECTIVE: This study aimed to create a high-fidelity model for training in the essential neurosurgical steps of fetoscopic spina bifida repair using synthetic materials. In addition, we aimed to obtain a cheap and easily reproducible model. STUDY DESIGN: We developed a 3-layered, silicon-based model that resemble the anatomic layers of a typical myelomeningocele lesion. It allows for filling of the cyst with fluid and conducting a water tightness test after repair. A compliant silicon ball mimics the uterine cavity and is fixed to a solid 3-dimensional printed base. The fetal back with the lesion (single-use) is placed inside the uterine ball, which is reusable and repairable to allow for practicing port insertion and fixation multiple times. Following cannula insertion, the uterus is insufflated and a clinical fetoscopic or robotic or prototype instruments can be used. Three skilled endoscopic surgeons each did 6 simulated fetoscopic repairs using the surgical steps of an open repair. The primary outcome was surgical success, which was determined by water tightness of the repair, operation time <180 minutes and an Objective Structured Assessment of Technical Skills score of ≥18 of 25. Skill retention was measured using a competence cumulative sum analysis of a composite binary outcome of surgical success. Secondary outcomes were cost and fabrication time of the model. RESULTS: We made a model that can be used to simulate the neurosurgical steps of spina bifida repair, including anatomic details, port insertion, placode release and descent, undermining of skin and muscular layer, and endoscopic suturing. The model was made using reusable 3-dimensional printed molds and easily accessible materials. The 1-time startup cost was €211, and each single-use, simulated myelomeningocele lesion cost €9.5 in materials and 50 minutes of working time. Two skilled endoscopic surgeons performed 6 simulated, 3-port fetoscopic repairs, whereas a third used a Da Vinci surgical robot. Operation times decreased by more than 30% from the first to the last trial. Six experiments per surgeon did not show an obvious Objective Structured Assessment of Technical Skills score improvement. Competence cumulative sum analysis confirmed competency for each surgeon. CONCLUSION: This high-fidelity, low-cost spina bifida model allows simulated dissection and closure of a myelomeningocele lesion. VIDEO ABSTRACT.

3D vs. 2D simulated fetoscopy for spina bifida repair: a quantitative motion analysis.

3D imaging technology is becoming more prominent every day. However, more validation is needed to understand the actual benefit of 3D versus conventional 2D vision. This work quantitatively investigates whether experts benefit from 3D vision during minimally invasive fetoscopic spina bifida (fSB) repair. A superiority study was designed involving one expert team ([Formula: see text] procedures prior) who performed six 2D and six 3D fSB repair simulations in a high-fidelity animal training model, using 3-port access. The 6D motion of the instruments was recorded. Among the motion metrics are total path length, smoothness, maximum speed, the modified Spectral Arc Length (SPARC), and Log Dimensionless Jerk (LDLJ). The primary clinical outcome is operation time (power 90%, 5% significance) using Sealed Envelope Ltd. 2012. Secondary clinical outcomes are water tightness of the repair, CO[Formula: see text] insufflation volume, and OSATS score. Findings show that total path length and LDLJ are considerably different. Operation time during 3D vision was found to be significantly shorter compared to 2D vision ([Formula: see text] vs. [Formula: see text] min; p [Formula: see text] 0.026). These results suggest enhanced performance with 3D vision during interrupted suturing in fetoscopic SBA repair. To confirm these results, a larger-scale follow-up study involving multiple experts and novice surgeons is recommended.

Lightfield hyperspectral imaging in neuro-oncology surgery: an IDEAL 0 and 1 study.

Introduction: Hyperspectral imaging (HSI) has shown promise in the field of intra-operative imaging and tissue differentiation as it carries the capability to provide real-time information invisible to the naked eye whilst remaining label free. Previous iterations of intra-operative HSI systems have shown limitations, either due to carrying a large footprint limiting ease of use within the confines of a neurosurgical theater environment, having a slow image acquisition time, or by compromising spatial/spectral resolution in favor of improvements to the surgical workflow. Lightfield hyperspectral imaging is a novel technique that has the potential to facilitate video rate image acquisition whilst maintaining a high spectral resolution. Our pre-clinical and first-in-human studies (IDEAL 0 and 1, respectively) demonstrate the necessary steps leading to the first in-vivo use of a real-time lightfield hyperspectral system in neuro-oncology surgery. Methods: A lightfield hyperspectral camera (Cubert Ultris ×50) was integrated in a bespoke imaging system setup so that it could be safely adopted into the open neurosurgical workflow whilst maintaining sterility. Our system allowed the surgeon to capture in-vivo hyperspectral data (155 bands, 350-1,000 nm) at 1.5 Hz. Following successful implementation in a pre-clinical setup (IDEAL 0), our system was evaluated during brain tumor surgery in a single patient to remove a posterior fossa meningioma (IDEAL 1). Feedback from the theater team was analyzed and incorporated in a follow-up design aimed at implementing an IDEAL 2a study. Results: Focusing on our IDEAL 1 study results, hyperspectral information was acquired from the cerebellum and associated meningioma with minimal disruption to the neurosurgical workflow. To the best of our knowledge, this is the first demonstration of HSI acquisition with 100+ spectral bands at a frame rate over 1Hz in surgery. Discussion: This work demonstrated that a lightfield hyperspectral imaging system not only meets the design criteria and specifications outlined in an IDEAL-0 (pre-clinical) study, but also that it can translate into clinical practice as illustrated by a successful first in human study (IDEAL 1). This opens doors for further development and optimisation, given the increasing evidence that hyperspectral imaging can provide live, wide-field, and label-free intra-operative imaging and tissue differentiation.

TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency.

Accurate segmentation of brain tumors from medical images is important for diagnosis and treatment planning, and it often requires multi-modal or contrast-enhanced images. However, in practice some modalities of a patient may be absent. Synthesizing the missing modality has a potential for filling this gap and achieving high segmentation performance. Existing methods often treat the synthesis and segmentation tasks separately or consider them jointly but without effective regularization of the complex joint model, leading to limited performance. We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain tumors end-to-end with high performance. First, we propose a dual-task-regularized generator that simultaneously obtains a synthesized target modality and a coarse segmentation, which leverages a tumor-aware synthesis loss with perceptibility regularization to minimize the high-level semantic domain gap between synthesized and real target modalities. Based on the synthesized image and the coarse segmentation, we further propose a dual-task segmentor that predicts a refined segmentation and error in the coarse segmentation simultaneously, where a consistency between these two predictions is introduced for regularization. Our TISS-Net was validated with two applications: synthesizing FLAIR images for whole glioma segmentation, and synthesizing contrast-enhanced T1 images for Vestibular Schwannoma segmentation. Experimental results showed that our TISS-Net largely improved the segmentation accuracy compared with direct segmentation from the available modalities, and it outperformed state-of-the-art image synthesis-based segmentation methods.

Synthetic white balancing for intra-operative hyperspectral imaging.

Purpose: Hyperspectral imaging shows promise for surgical applications to non-invasively provide spatially resolved, spectral information. For calibration purposes, a white reference image of a highly reflective Lambertian surface should be obtained under the same imaging conditions. Standard white references are not sterilizable and so are unsuitable for surgical environments. We demonstrate the necessity for in situ white references and address this by proposing a novel, sterile, synthetic reference construction algorithm. Approach: The use of references obtained at different distances and lighting conditions to the subject were examined. Spectral and color reconstructions were compared with standard measurements qualitatively and quantitatively, using ΔE and normalized RMSE, respectively. The algorithm forms a composite image from a video of a standard sterile ruler, whose imperfect reflectivity is compensated for. The reference is modeled as the product of independent spatial and spectral components, and a scalar factor accounting for gain, exposure, and light intensity. Evaluation of synthetic references against ideal but non-sterile references is performed using the same metrics alongside pixel-by-pixel errors. Finally, intraoperative integration is assessed though cadaveric experiments. Results: Improper white balancing leads to increases in all quantitative and qualitative errors. Synthetic references achieve median pixel-by-pixel errors lower than 6.5% and produce similar reconstructions and errors to an ideal reference. The algorithm integrated well into surgical workflow, achieving median pixel-by-pixel errors of 4.77% while maintaining good spectral and color reconstruction. Conclusions: We demonstrate the importance of in situ white referencing and present a novel synthetic referencing algorithm. This algorithm is suitable for surgery while maintaining the quality of classical data reconstruction.

Diffusion MRI of the facial-vestibulocochlear nerve complex: a prospective clinical validation study.

OBJECTIVES: Surgical planning of vestibular schwannoma surgery would benefit greatly from a robust method of delineating the facial-vestibulocochlear nerve complex with respect to the tumour. This study aimed to optimise a multi-shell readout-segmented diffusion-weighted imaging (rs-DWI) protocol and develop a novel post-processing pipeline to delineate the facial-vestibulocochlear complex within the skull base region, evaluating its accuracy intraoperatively using neuronavigation and tracked electrophysiological recordings. METHODS: In a prospective study of five healthy volunteers and five patients who underwent vestibular schwannoma surgery, rs-DWI was performed and colour tissue maps (CTM) and probabilistic tractography of the cranial nerves were generated. In patients, the average symmetric surface distance (ASSD) and 95% Hausdorff distance (HD-95) were calculated with reference to the neuroradiologist-approved facial nerve segmentation. The accuracy of patient results was assessed intraoperatively using neuronavigation and tracked electrophysiological recordings. RESULTS: Using CTM alone, the facial-vestibulocochlear complex of healthy volunteer subjects was visualised on 9/10 sides. CTM were generated in all 5 patients with vestibular schwannoma enabling the facial nerve to be accurately identified preoperatively. The mean ASSD between the annotators' two segmentations was 1.11 mm (SD 0.40) and the mean HD-95 was 4.62 mm (SD 1.78). The median distance from the nerve segmentation to a positive stimulation point was 1.21 mm (IQR 0.81-3.27 mm) and 2.03 mm (IQR 0.99-3.84 mm) for the two annotators, respectively. CONCLUSIONS: rs-DWI may be used to acquire dMRI data of the cranial nerves within the posterior fossa. CLINICAL RELEVANCE STATEMENT: Readout-segmented diffusion-weighted imaging and colour tissue mapping provide 1-2 mm spatially accurate imaging of the facial-vestibulocochlear nerve complex, enabling accurate preoperative localisation of the facial nerve. This study evaluated the technique in 5 healthy volunteers and 5 patients with vestibular schwannoma. KEY POINTS: • Readout-segmented diffusion-weighted imaging (rs-DWI) with colour tissue mapping (CTM) visualised the facial-vestibulocochlear nerve complex on 9/10 sides in 5 healthy volunteer subjects. • Using rs-DWI and CTM, the facial nerve was visualised in all 5 patients with vestibular schwannoma and within 1.21-2.03 mm of the nerve's true intraoperative location. • Reproducible results were obtained on different scanners.

Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review.

Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging biomarkers for these tumors that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase, and Medline were searched from 1 January 2000 to 7 March 2022, to systematically identify relevant publications in this area. All studies that used an imaging tool and found an association with a growth-related factor, including molecular markers, grade, survival, growth/progression, recurrence, and treatment outcomes, were included in this review. We included 42 studies, comprising 22 studies (50%) of patients with meningioma; 17 studies (38.6%) of patients with pituitary tumors; three studies (6.8%) of patients with vestibular schwannomas; and two studies (4.5%) of patients with solitary fibrous tumors. The included studies were explicitly and narratively analyzed according to tumor type and imaging tool. The risk of bias and concerns regarding applicability were assessed using QUADAS-2. Most studies (41/44) used statistics-based analysis methods, and a small number of studies (3/44) used machine learning. Our review highlights an opportunity for future work to focus on machine learning-based deep feature identification as biomarkers, combining various feature classes such as size, shape, and intensity. Systematic Review Registration: PROSPERO, CRD42022306922.

Intraoperative bowel perfusion assessment methods and their effects on anastomotic leak rates: meta-analysis.

BACKGROUND: Anastomotic leak is one of the most feared complications of colorectal surgery, and probably linked to poor blood supply to the anastomotic site. Several technologies have been described for intraoperative assessment of bowel perfusion. This systematic review and meta-analysis aimed to evaluate the most frequently used bowel perfusion assessment modalities in elective colorectal procedures, and to assess their associated risk of anastomotic leak. Technologies included indocyanine green fluorescence angiography, diffuse reflectance spectroscopy, laser speckle contrast imaging, and hyperspectral imaging. METHODS: The review was preregistered with PROSPERO (CRD42021297299). A comprehensive literature search was performed using Embase, MEDLINE, Cochrane Library, Scopus, and Web of Science. The final search was undertaken on 29 July 2022. Data were extracted by two reviewers and the MINORS criteria were applied to assess the risk of bias. RESULTS: Some 66 eligible studies involving 11 560 participants were included. Indocyanine green fluorescence angiography was most used with 10 789 participants, followed by diffuse reflectance spectroscopy with 321, hyperspectral imaging with 265, and laser speckle contrast imaging with 185. In the meta-analysis, the total pooled effect of an intervention on anastomotic leak was 0.05 (95 per cent c.i. 0.04 to 0.07) in comparison with 0.10 (0.08 to 0.12) without. Use of indocyanine green fluorescence angiography, hyperspectral imaging, or laser speckle contrast imaging was associated with a significant reduction in anastomotic leak. CONCLUSION: Bowel perfusion assessment reduced the incidence of anastomotic leak, with intraoperative indocyanine green fluorescence angiography, hyperspectral imaging, and laser speckle contrast imaging all demonstrating comparable results.

Development and validation of a flexible fetoscope for fetoscopic laser coagulation.

PURPOSE: Fetoscopic laser coagulation for twin-to-twin transfusion syndrome is challenging for anterior placenta due to the rigidity of current tools. The capacity to keep entry port forces minimal is critical for this procedure, as is optimal coagulation distance and orientation. This work introduces technological tools to this end. METHODS: A novel fetoscope is presented with a rigid shaft and a flexible steerable segment at the distal end. The steerable segment can bend up to 90[Formula: see text] even when loaded with a laser fiber. An artificial pneumatic muscle makes such acute bending possible while allowing for a low-weight and disposable device. RESULTS: The flexible fetoscope was validated in a custom-made phantom model to measure visual range and coagulation efficacy. The flexible fetoscope shows promising results when compared to a clinical rigid curved fetoscope to reach anterior targets. The new fetoscope was then evaluated in vivo (pregnant ewe) where it successfully coagulated placental vasculature. CONCLUSION: The flexible fetoscope improved the ability to achieve optimal coagulation angle and distance on anteriorly located targets. The fetoscope also showed the potential to lead fetoscopic laser coagulation and other fetal surgical procedures toward safer and more effective interventions.

Spatial gradient consistency for unsupervised learning of hyperspectral demosaicking: application to surgical imaging.

PURPOSE: Hyperspectral imaging has the potential to improve intraoperative decision making if tissue characterisation is performed in real-time and with high-resolution. Hyperspectral snapshot mosaic sensors offer a promising approach due to their fast acquisition speed and compact size. However, a demosaicking algorithm is required to fully recover the spatial and spectral information of the snapshot images. Most state-of-the-art demosaicking algorithms require ground-truth training data with paired snapshot and high-resolution hyperspectral images, but such imagery pairs with the exact same scene are physically impossible to acquire in intraoperative settings. In this work, we present a fully unsupervised hyperspectral image demosaicking algorithm which only requires exemplar snapshot images for training purposes. METHODS: We regard hyperspectral demosaicking as an ill-posed linear inverse problem which we solve using a deep neural network. We take advantage of the spectral correlation occurring in natural scenes to design a novel inter spectral band regularisation term based on spatial gradient consistency. By combining our proposed term with standard regularisation techniques and exploiting a standard data fidelity term, we obtain an unsupervised loss function for training deep neural networks, which allows us to achieve real-time hyperspectral image demosaicking. RESULTS: Quantitative results on hyperspetral image datasets show that our unsupervised demosaicking approach can achieve similar performance to its supervised counter-part, and significantly outperform linear demosaicking. A qualitative user study on real snapshot hyperspectral surgical images confirms the results from the quantitative analysis. CONCLUSION: Our results suggest that the proposed unsupervised algorithm can achieve promising hyperspectral demosaicking in real-time thus advancing the suitability of the modality for intraoperative use.

CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation.

Domain Adaptation (DA) has recently been of strong interest in the medical imaging community. While a large variety of DA techniques have been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT1) MR imaging. However, there is growing interest in using non-contrast imaging sequences such as high-resolution T2 (hrT2) imaging. For this reason, we established an unsupervised cross-modality segmentation benchmark. The training dataset provides annotated ceT1 scans (N=105) and unpaired non-annotated hrT2 scans (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 scans as provided in the testing set (N=137). This problem is particularly challenging given the large intensity distribution gap across the modalities and the small volume of the structures. A total of 55 teams from 16 countries submitted predictions to the validation leaderboard. Among them, 16 teams from 9 different countries submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice score - VS: 88.4%; Cochleas: 85.7%) and close to full supervision (median Dice score - VS: 92.5%; Cochleas: 87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.

Intraoperative Needle Tip Tracking with an Integrated Fibre-Optic Ultrasound Sensor.

Ultrasound is an essential tool for guidance of many minimally-invasive surgical and interventional procedures, where accurate placement of the interventional device is critical to avoid adverse events. Needle insertion procedures for anaesthesia, fetal medicine and tumour biopsy are commonly ultrasound-guided, and misplacement of the needle may lead to complications such as nerve damage, organ injury or pregnancy loss. Clear visibility of the needle tip is therefore critical, but visibility is often precluded by tissue heterogeneities or specular reflections from the needle shaft. This paper presents the in vitro and ex vivo accuracy of a new, real-time, ultrasound needle tip tracking system for guidance of fetal interventions. A fibre-optic, Fabry-Pérot interferometer hydrophone is integrated into an intraoperative needle and used to localise the needle tip within a handheld ultrasound field. While previous, related work has been based on research ultrasound systems with bespoke transmission sequences, the new system-developed under the ISO 13485 Medical Devices quality standard-operates as an adjunct to a commercial ultrasound imaging system and therefore provides the image quality expected in the clinic, superimposing a cross-hair onto the ultrasound image at the needle tip position. Tracking accuracy was determined by translating the needle tip to 356 known positions in the ultrasound field of view in a tank of water, and by comparison to manual labelling of the the position of the needle in B-mode US images during an insertion into an ex vivo phantom. In water, the mean distance between tracked and true positions was 0.7 ± 0.4 mm with a mean repeatability of 0.3 ± 0.2 mm. In the tissue phantom, the mean distance between tracked and labelled positions was 1.1 ± 0.7 mm. Tracking performance was found to be independent of needle angle. The study demonstrates the performance and clinical compatibility of ultrasound needle tracking, an essential step towards a first-in-human study.

Ultrathin, high-speed, all-optical photoacoustic endomicroscopy probe for guiding minimally invasive surgery.

Photoacoustic (PA) endoscopy has shown significant potential for clinical diagnosis and surgical guidance. Multimode fibres (MMFs) are becoming increasingly attractive for the development of miniature endoscopy probes owing to their ultrathin size, low cost and diffraction-limited spatial resolution enabled by wavefront shaping. However, current MMF-based PA endomicroscopy probes are either limited by a bulky ultrasound detector or a low imaging speed that hindered their usability. In this work, we report the development of a highly miniaturised and high-speed PA endomicroscopy probe that is integrated within the cannula of a 20 gauge medical needle. This probe comprises a MMF for delivering the PA excitation light and a single-mode optical fibre with a plano-concave microresonator for ultrasound detection. Wavefront shaping with a digital micromirror device enabled rapid raster-scanning of a focused light spot at the distal end of the MMF for tissue interrogation. High-resolution PA imaging of mouse red blood cells covering an area 100 µm in diameter was achieved with the needle probe at ∼3 frames per second. Mosaicing imaging was performed after fibre characterisation by translating the needle probe to enlarge the field-of-view in real-time. The developed ultrathin PA endomicroscopy probe is promising for guiding minimally invasive surgery by providing functional, molecular and microstructural information of tissue in real-time.

Cross-Modality Image Registration Using a Training-Time Privileged Third Modality.

In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusion-weighted scans with high b-value (DWI [Formula: see text]). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI [Formula: see text]) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWI [Formula: see text], to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI [Formula: see text] and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI [Formula: see text] and T2w in this challenging application.

International Consensus Guidelines for Adjuvant Radiation Therapy for Bladder Cancer After Radical Cystectomy: Update From an IBIS Workgroup.

PURPOSE: In 2016, international consensus clinical target volume (CTV) guidelines for adjuvant radiation treatment after radical cystectomy in patients with muscle-invasive bladder cancer with high risk for locoregional failure (LRF) were published. A subsequent external validation study recommended several CTV optimizations (CTV-OPT). This study aimed to update international consensus guidelines based on new clinical experiences. METHODS AND MATERIALS: Phase 1 (delineation interobserver variability): Four observers delineated the CTV of 9 patients post radical cystectomy, as in clinical practice. Interobserver agreement in contouring was evaluated using volume- and κ-statistics. Phase 2 (pattern of failure analysis): Among a prospective cohort of 72 patients treated with adjuvant radiation treatment, 11 developed LRF (10 available for review). LRFs were mapped in predefined pelvic subsites (ie, common, external and internal iliac, obturator and presacral node regions, and cystectomy bed), and their distance to CTV-OPT was measured. The actual delivered dose at each relapse site was calculated. Phase 3 (review CTV): Based on the results of phase 1 and 2, 5 senior radiation-oncologists (International Bladder Investigator Society) reviewed the published CTV borders and provided an update when indicated. RESULTS: Phase 1: The mean overall κ-value was 0.66 (range, 0.60-0.70), indicating substantial overall agreement per Landis-Koch criteria. Specific κ-values per area indicated for the common iliac and obturator node regions only slight and moderate variability, respectively. Phase 2: Thirteen out of 16 LRFs centers were not included in the CTV-OPT. Ten LRF sites received a median dose <45 Gy, of which 6 were located in the cystectomy bed that was not included in the CTV because of negative radical cystectomy margins. Phase 3: Key recommendations by the panel were to include the entire common iliac node region and the cystectomy bed regardless of surgical margin status and a reaffirmation to not crop the CTV out of bowel. CONCLUSIONS: International consensus guidelines were updated.