Search

Distant metastases (DM) refer to the dissemination of tumors, usually, beyond the organ where the tumor originated. They are the leading cause of death in patients with soft-tissue sarcomas (STSs). Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of STSs. It is difficult to determine from imaging studies which STS patients will develop metastases. 'Radiomics' refers to the extraction and analysis of quantitative features from medical images and it has been employed to help identify such tumors. The state-of-the-art in radiomics is based on convolutional neural networks (CNNs). Most CNNs are designed for single-modality imaging data (CT or PET alone) and do not exploit the information embedded in PET-CT where there is a combination of an anatomical and functional imaging modality. Furthermore, most radiomic methods rely on manual input from imaging specialists for tumor delineation, definition and selection of radiomic features. This approach, however, may not be scalable to tumors with complex boundaries and where there are multiple other sites of disease. We outline a new 3D CNN to help predict DM in STS patients from PET-CT data. The 3D CNN uses a constrained feature learning module and a hierarchical multi-modality feature learning module that leverages the complementary information from the modalities to focus on semantically important regions. Our results on a public PET-CT dataset of STS patients show that multi-modal information improves the ability to identify those patients who develop DM. Further our method outperformed all other related state-of-the-art methods., Comment: Under Review

Detection of individual molecules is the ultimate goal of any chemical sensor. In the case of gas detection, such resolution has been achieved in advanced nanoscale electronic solid-state sensors, but it has not been possible so far in integrated photonic devices, where the weak light-molecule interaction is typically hidden by noise. Here, we demonstrate a scheme to generate ultrasensitive down-conversion four-wave-mixing (FWM) in a graphene bipolar-junction-transistor heterogeneous D-shaped fiber. In the communication band, the FWM conversion efficiency can change steeply when the graphene Fermi level approaches 0.4 eV. In this condition, we exploit our unique two-step optoelectronic heterodyne detection scheme, and we achieve real-time individual gas molecule detection in vacuum. Such combination of graphene strong nonlinearities, electrical tunability, and all-fiber integration paves the way toward the design of versatile high-performance graphene photonic devices.

The identification of melanoma involves an integrated analysis of skin lesion images acquired using the clinical and dermoscopy modalities. Dermoscopic images provide a detailed view of the subsurface visual structures that supplement the macroscopic clinical images. Melanoma diagnosis is commonly based on the 7-point visual category checklist (7PC). The 7PC contains intrinsic relationships between categories that can aid classification, such as shared features, correlations, and the contributions of categories towards diagnosis. Manual classification is subjective and prone to intra- and interobserver variability. This presents an opportunity for automated methods to improve diagnosis. Current state-of-the-art methods focus on a single image modality and ignore information from the other, or do not fully leverage the complementary information from both modalities. Further, there is not a method to exploit the intercategory relationships in the 7PC. In this study, we address these issues by proposing a graph-based intercategory and intermodality network (GIIN) with two modules. A graph-based relational module (GRM) leverages intercategorical relations, intermodal relations, and prioritises the visual structure details from dermoscopy by encoding category representations in a graph network. The category embedding learning module (CELM) captures representations that are specialised for each category and support the GRM. We show that our modules are effective at enhancing classification performance using a public dataset of dermoscopy-clinical images, and show that our method outperforms the state-of-the-art at classifying the 7PC categories and diagnosis.

Objective: Deep Learning-based Radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year Progression-Free Survival (PFS) in Nasopharyngeal Carcinoma (NPC) using pretreatment PET/CT. Methods: A total of 257 patients (170/87 in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural network was optimized to extract deep features from pretreatment PET/CT images and predict the probability of 5-year PFS. TNM stage, as a high-level clinical feature, could be integrated into our DLR model to further improve the prognostic performance. To compare conventional radiomics and DLR, 1456 handcrafted features were extracted, and optimal conventional radiomics methods were selected from 54 cross-combinations of 6 feature selection methods and 9 classification methods. In addition, risk group stratification was performed with clinical signature, conventional radiomics signature, and DLR signature. Results: Our multi-modality DLR model using both PET and CT achieved higher prognostic performance than the optimal conventional radiomics method. Furthermore, the multi-modality DLR model outperformed single-modality DLR models using only PET or only CT. For risk group stratification, the conventional radiomics signature and DLR signature enabled significant differences between the high- and low-risk patient groups in both internal and external cohorts, while the clinical signature failed in the external cohort. Conclusion: Our study identified potential prognostic tools for survival prediction in advanced NPC, suggesting that DLR could provide complementary values to the current TNM staging., Comment: Accepted at Frontiers in Oncology

Deformable image registration is fundamental for many medical image analyses. A key obstacle for accurate image registration lies in image appearance variations such as the variations in texture, intensities, and noise. These variations are readily apparent in medical images, especially in brain images where registration is frequently used. Recently, deep learning-based registration methods (DLRs), using deep neural networks, have shown computational efficiency that is several orders of magnitude faster than traditional optimization-based registration methods (ORs). DLRs rely on a globally optimized network that is trained with a set of training samples to achieve faster registration. DLRs tend, however, to disregard the target-pair-specific optimization inherent in ORs and thus have degraded adaptability to variations in testing samples. This limitation is severe for registering medical images with large appearance variations, especially since few existing DLRs explicitly take into account appearance variations. In this study, we propose an Appearance Adjustment Network (AAN) to enhance the adaptability of DLRs to appearance variations. Our AAN, when integrated into a DLR, provides appearance transformations to reduce the appearance variations during registration. In addition, we propose an anatomy-constrained loss function through which our AAN generates anatomy-preserving transformations. Our AAN has been purposely designed to be readily inserted into a wide range of DLRs and can be trained cooperatively in an unsupervised and end-to-end manner. We evaluated our AAN with three state-of-the-art DLRs on three well-established public datasets of 3D brain magnetic resonance imaging (MRI). The results show that our AAN consistently improved existing DLRs and outperformed state-of-the-art ORs on registration accuracy, while adding a fractional computational load to existing DLRs., Comment: Published at NeuroImage

Accurate characterisation of visual attributes such as spiculation, lobulation, and calcification of lung nodules is critical in cancer management. The characterisation of these attributes is often subjective, which may lead to high inter- and intra-observer variability. Furthermore, lung nodules are often heterogeneous in the cross-sectional image slices of a 3D volume. Current state-of-the-art methods that score multiple attributes rely on deep learning-based multi-task learning (MTL) schemes. These methods, however, extract shared visual features across attributes and then examine each attribute without explicitly leveraging their inherent intercorrelations. Furthermore, current methods either treat each slice with equal importance without considering their relevance or heterogeneity, which limits performance. In this study, we address these challenges with a new convolutional neural network (CNN)-based MTL model that incorporates multiple attention-based learning modules to simultaneously score 9 visual attributes of lung nodules in computed tomography (CT) image volumes. Our model processes entire nodule volumes of arbitrary depth and uses a slice attention module to filter out irrelevant slices. We also introduce cross-attribute and attribute specialisation attention modules that learn an optimal amalgamation of meaningful representations to leverage relationships between attributes. We demonstrate that our model outperforms previous state-of-the-art methods at scoring attributes using the well-known public LIDC-IDRI dataset of pulmonary nodules from over 1,000 patients. Our model also performs competitively when repurposed for benign-malignant classification. Our attention modules also provide easy-to-interpret weights that offer insights into the predictions of the model.

Magneto-optical effect refers to a rotation of polarization plane, which has been widely studied in traditional ferromagnetic metal and insulator films and scarcely in two-dimensional layered materials. Here we uncover a new nonreciprocal magneto-inelastic light scattering effect in ferromagnetic few-layer CrI3. We observed a rotation of the polarization plane of inelastic light scattering between -20o and +60o that are tunable by an out-of-plane magnetic field from -2.5 to 2.5 T. It is experimentally observed that the degree of polarization can be magnetically manipulated between -20% and 85%. This work raises a new magneto-optical phenomenon and could create opportunities of applying 2D ferromagnetic materials in Raman lasing, topological photonics, and magneto-optical modulator for information transport and storage.

Optical isolators and circulators are indispensable for photonic integrated circuits (PICs). Despite of significant progress in silicon-on-insulator (SOI) platforms, integrated optical isolators and circulators have been rarely reported on silicon nitride (SiN) platforms. In this paper, we report monolithic integration of magneto-optical (MO) isolators on SiN platforms with record high performances based on standard silicon photonics foundry process and magneto-optical thin film deposition. We successfully grow high quality MO garnet thin films on SiN with large Faraday rotation up to -5900 deg/cm. We show a superior magneto-optical figure of merit (FoM) of MO/SiN waveguides compared to that of MO/SOI in an optimized device design. We demonstrate TM/TE mode broadband and narrow band optical isolators and circulators on SiN with high isolation ratio, low cross talk and low insertion loss. In particular, we observe 1 dB insertion loss and 28 dB isolation ratio in a SiN racetrack resonator-based isolator at 1570.2 nm wavelength. The low thermo-optic coefficient of SiN also ensures excellent temperature stability of the device. Our work paves the way for integration of high performance nonreciprocal photonic devices on SiN platforms.

Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection with PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, there is not an accurate automated segmentation method. Segmentation tends to be done manually by different imaging experts and it is labor-intensive and prone to errors and inconsistency. Previous automated segmentation methods largely focused on fusing information that is extracted separately from the PET and CT modalities, with the underlying assumption that each modality contains complementary information. However, these methods do not fully exploit the high PET tumor sensitivity that can guide the segmentation. We introduce a multimodal spatial attention module (MSAM) that automatically learns to emphasize regions (spatial areas) related to tumors and suppress normal regions with physiologic high-uptake. The resulting spatial attention maps are subsequently employed to target a convolutional neural network (CNN) for segmentation of areas with higher tumor likelihood. Our MSAM can be applied to common backbone architectures and trained end-to-end. Our experimental results on two clinical PET-CT datasets of non-small cell lung cancer (NSCLC) and soft tissue sarcoma (STS) validate the effectiveness of the MSAM in these different cancer types. We show that our MSAM, with a conventional U-Net backbone, surpasses the state-of-the-art lung tumor segmentation approach by a margin of 7.6% in Dice similarity coefficient (DSC).

The accurate classification of mass lesions in the adrenal glands (adrenal masses), detected with computed tomography (CT), is important for diagnosis and patient management. Adrenal masses can be benign or malignant and benign masses have varying prevalence. Classification methods based on convolutional neural networks (CNNs) are the state-of-the-art in maximizing inter-class differences in large medical imaging training datasets. The application of CNNs, to adrenal masses is challenging due to large intra-class variations, large inter-class similarities and imbalanced training data due to the size of the mass lesions. We developed a deep multi-scale resemblance network (DMRN) to overcome these limitations and leveraged paired CNNs to evaluate the intra-class similarities. We used multi-scale feature embedding to improve the inter-class separability by iteratively combining complementary information produced at different scales of the input to create structured feature descriptors. We augmented the training data with randomly sampled paired adrenal masses to reduce the influence of imbalanced training data. We used 229 CT scans of patients with adrenal masses for evaluation. In a five-fold cross-validation, our method had the best results (89.52% in accuracy) when compared to the state-of-the-art methods (p<0.05). We conducted a generalizability analysis of our method on the ImageCLEF 2016 competition dataset for medical subfigure classification, which consists of a training set of 6,776 images and a test set of 4,166 images across 30 classes. Our method achieved better classification performance (85.90% in accuracy) when compared to the existing methods and was competitive when compared with methods that require additional training data (1.47% lower in accuracy). Our DMRN sub-classified adrenal masses on CT and was superior to state-of-the-art approaches., Comment: 22 pages

'Radiomics' is a method that extracts mineable quantitative features from radiographic images. These features can then be used to determine prognosis, for example, predicting the development of distant metastases (DM). Existing radiomics methods, however, require complex manual effort including the design of hand-crafted radiomic features and their extraction and selection. Recent radiomics methods, based on convolutional neural networks (CNNs), also require manual input in network architecture design and hyper-parameter tuning. Radiomic complexity is further compounded when there are multiple imaging modalities, for example, combined positron emission tomography - computed tomography (PET-CT) where there is functional information from PET and complementary anatomical localization information from computed tomography (CT). Existing multi-modality radiomics methods manually fuse the data that are extracted separately. Reliance on manual fusion often results in sub-optimal fusion because they are dependent on an 'expert's' understanding of medical images. In this study, we propose a multi-modality neural architecture search method (MM-NAS) to automatically derive optimal multi-modality image features for radiomics and thus negate the dependence on a manual process. We evaluated our MM-NAS on the ability to predict DM using a public PET-CT dataset of patients with soft-tissue sarcomas (STSs). Our results show that our MM-NAS had a higher prediction accuracy when compared to state-of-the-art radiomics methods., Comment: Accepted by MICCAI 2020

Two-dimensional (2D) transition metal dichalcogenides (TMDs) with tantalizing layer-dependent electronic and optical properties have emerged as a new paradigm for integrated flat opto-electronic devices. However, daunting challenges remain in deterministic fabrication of TMD layers with demanded shapes and thicknesses as well as light field manipulation in such atomic-thick layers with vanishingly small thicknesses compared to the wavelength. Here, we demonstrate ultra-sensitive light field manipulation in full visible ranges based on laser exfoliating MoS2 layers with nanometric precisions. The nontrivial interfacial phase shifts stemming from the unique dispersion of MoS2 layers integrated on the metallic substrate empower an ultra-sensitive resonance manipulation up to 12.8 nm per MoS2 layer across the entire visible bands, which is more than five times larger than their counterparts. The interlayer van der Waals interactions endow a laser exfoliation method for on-demand patterning MoS2 with atomic thickness precisions and subwavelength feature sizes in a facile and lithography-free fashion. With this, nanometric flat color prints and further binocular stereoscopic views by multi-perspective diffractive images can be realized. Our results with demonstrated practicality unlock full potentials and pave the way for widespread applications of emerging 2D flat optics.

Dynamic medical imaging is usually limited in application due to the large radiation doses and longer image scanning and reconstruction times. Existing methods attempt to reduce the dynamic sequence by interpolating the volumes between the acquired image volumes. However, these methods are limited to either 2D images and/or are unable to support large variations in the motion between the image volume sequences. In this paper, we present a spatiotemporal volumetric interpolation network (SVIN) designed for 4D dynamic medical images. SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures. We also introduce an adaptive multi-scale architecture to capture the volumetric large anatomy motions. Experimental results demonstrated that our SVIN outperformed state-of-the-art temporal medical interpolation methods and natural video interpolation methods that have been extended to support volumetric images. Our ablation study further exemplified that our motion network was able to better represent the large functional motion compared with the state-of-the-art unsupervised medical registration methods., Comment: 10 pages, 8 figures, Conference on Computer Vision and Pattern Recognition (CVPR) 2020

Valley pseudospin in two-dimensional (2D) transition-metal dichalcogenides (TMDs) allows optical control of spin-valley polarization and intervalley quantum coherence. Defect states in TMDs give rise to new exciton features and theoretically exhibit spin-valley polarization; however, experimental achievement of this phenomenon remains challenges. Here, we report unambiguous valley pseudospin of defect-bound localized excitons in CVD-grown monolayer MoS2; enhanced valley Zeeman splitting with an effective g-factor of -6.2 is observed. Our results reveal that all five d-orbitals and the increased effective electron mass contribute to the band shift of defect states, demonstrating a new physics of the magnetic responses of defect-bound localized excitons, strikingly different from that of A excitons. Our work paves the way for the manipulation of the spin-valley degrees of freedom through defects toward valleytronic devices.

Photonic topological insulators (PTIs) exhibit robust photonic edge states protected by band topology, similar to electronic edge states in topological band insulators. Standard band theory does not apply to amorphous phases of matter, which are formed by non-crystalline lattices with no long-range positional order but only short-range order. Among other interesting properties, amorphous media exhibit transitions between glassy and liquid phases, accompanied by dramatic changes in short-range order. Here, we experimentally investigate amorphous variants of a Chern-number-based PTI. By tuning the disorder strength in the lattice, we demonstrate that photonic topological edge states can persist into the amorphous regime, prior to the glass-to-liquid transition. After the transition to a liquid-like lattice configuration, the signatures of topological edge states disappear. This interplay between topology and short-range order in amorphous lattices paves the way for new classes of non-crystalline topological photonic materials., Comment: 13 pages, 4 figures

Spiking Neural Networks (SNNs) are brain-inspired, event-driven machine learning algorithms that have been widely recognized in producing ultra-high-energy-efficient hardware. Among existing SNNs, unsupervised SNNs based on synaptic plasticity, especially Spike-Timing-Dependent Plasticity (STDP), are considered to have great potential in imitating the learning process of the biological brain. Nevertheless, the existing STDP-based SNNs have limitations in constrained learning capability and/or slow learning speed. Most STDP-based SNNs adopted a slow-learning Fully-Connected (FC) architecture and used a sub-optimal vote-based scheme for spike decoding. In this paper, we overcome these limitations with: 1) a design of high-parallelism network architecture, inspired by the Inception module in Artificial Neural Networks (ANNs); 2) use of a Vote-for-All (VFA) decoding layer as a replacement to the standard vote-based spike decoding scheme, to reduce the information loss in spike decoding and, 3) a proposed adaptive repolarization (resetting) mechanism that accelerates SNNs' learning by enhancing spiking activities. Our experimental results on two established benchmark datasets (MNIST/EMNIST) show that our network architecture resulted in superior performance compared to the widely used FC architecture and a more advanced Locally-Connected (LC) architecture, and that our SNN achieved competitive results with state-of-the-art unsupervised SNNs (95.64%/80.11% accuracy on the MNIST/EMNISE dataset) while having superior learning efficiency and robustness against hardware damage. Our SNN achieved great classification accuracy with only hundreds of training iterations, and random destruction of large numbers of synapses or neurons only led to negligible performance degradation., Comment: Published at Neurocomputing

Chiral nanophotonic devices are promising candidates for chiral molecules sensing, polarization diverse nanophotonics and display technologies. Active chiral nanophotonic devices, where the optical chirality can be controlled by an external stimulus has triggered great research interest. However, efficient modulation of the optical chirality has been challenging. Here, we demonstrate switching of the extrinsic chirality by applied magnetic fields in a magneto-plasmonic metasurface device based on a magneto-optical oxide material, Ce1Y2Fe5O12 (Ce:YIG). Thanks to the low optical loss and strong magneto-optical effect of Ce:YIG, we experimentally demonstrated a giant and continuous far-field circular dichroism (CD) modulation by applied magnetic fields from -0.65{\deg} to +1.9{\deg} at 950 nm wavelength under glancing incident conditions. The far field CD modulation is due to both magneto-optical circular dichroism and near-field modulation of the superchiral fields by applied magnetic fields. Finally, we demonstrate magnetic field tunable chiral imaging in millimeter-scale magneto-plasmonic metasurfaces fabricated using self-assembly. Our results provide a new way for achieving planar integrated, large-scale and active chiral metasurfaces for polarization diverse nanophotonics.

At photonic Dirac points, electromagnetic waves are governed by the same equations as two-component massless relativistic fermions. However, photonic Dirac points are known to occur in pairs in "photonic graphene" and other similar photonic crystals, which necessitates special precautions to excite only states near one of the Dirac points. Systems hosting unpaired photonic Dirac points are significantly harder to realize, as they require broken time-reversal symmetry. Here, we report on the first observation of an unpaired Dirac point in a planar two-dimensional photonic crystal. The structure incorporates gyromagnetic materials, which break time-reversal symmetry; the unpaired Dirac point occurs when a parity-breaking parameter is fine-tuned to a topological transition between a photonic Chern insulator and a conventional photonic insulator phase. Evidence for the unpaired Dirac point is provided by transmission and field-mapping experiments, including a demonstration of strongly non-reciprocal reflection. This photonic crystal is suitable for investigating the unique features of two-dimensional Dirac states, such as one-way Klein tunneling.

Long-range magnetic orders in atomically thin ferromagnetic CrI3 give rise to new fascinating physics and application perspectives. The physical properties of two-dimensional (2D) ferromagnetism CrI3 are significantly influenced by interlayer spacing and stacking order, which are sensitive to the hydrostatic pressure and external environments. However, there remains debate on the stacking order at low temperature. Here, we study the interlayer coupling and stacking order of non-encapsulated 2-5 layer and bulk CrI3 at 10 K by Raman spectroscopy; demonstrate a rhombohedral stacking in both antiferromagnetic and ferromagnetic CrI3. The opposite helicity dependence of Ag and Eg modes arising from phonon symmetry further validate the rhombohedral stacking. An anomalous temperature-dependent behavior is observed due to spin-phonon coupling below 60 K. Our work provides insights into the interlayer coupling and stacking orders of 2D ferromagnetic materials.

Automated segmentation of the optic cup and disk on retinal fundus images is fundamental for the automated detection / analysis of glaucoma. Traditional segmentation approaches depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to be adapt to the clinical environment. Recently, deep learning methods based on fully convolutional networks (FCNs) have been successful in resolving segmentation problems. However, the reliance on large annotated training data is problematic when dealing with medical images. If a sufficient amount of annotated training data to cover all possible variations is not available, FCNs do not provide accurate segmentation. In addition, FCNs have a large receptive field in the convolutional layers, and hence produce coarse outputs of boundaries. Hence, we propose a new fully automated method that we refer to as a dual-stage fully convolutional networks (DSFCN). Our approach leverages deep residual architectures and FCNs and learns and infers the location of the optic cup and disk in a step-wise manner with fine-grained details. During training, our approach learns from the training data and the estimated results derived from the previous iteration. The ability to learn from the previous iteration optimizes the learning of the optic cup and the disk boundaries. During testing (prediction), DSFCN uses test (input) images and the estimated probability map derived from previous iterations to gradually improve the segmentation accuracy. Our method achieved an average Dice co-efficient of 0.8488 and 0.9441 for optic cup and disk segmentation and an area under curve (AUC) of 0.9513 for glaucoma detection., Comment: REFUGE Challenge

Integrated optical isolators have been a longstanding challenge for photonic integrated circuits (PIC). An ideal integrated optical isolator for PIC should be made by a monolithic process, have a small footprint, exhibit broadband and polarization-diverse operation, and be compatible with multiple materials platforms. Despite significant progress, the optical isolators reported so far do not meet all these requirements. In this article we present monolithically integrated broadband magneto-optical isolators on silicon and silicon nitride (SiN) platforms operating for both TE and TM modes with record high performances, fulfilling all the essential characteristics for PIC applications. In particular, we demonstrate fully-TE broadband isolators by depositing high quality magneto-optical garnet thin films on the sidewalls of Si and SiN waveguides, a critical result for applications in TE-polarized on-chip lasers and amplifiers. This work demonstrates monolithic integration of high performance optical isolators on chip for polarization-diverse silicon photonic systems, enabling new pathways to impart nonreciprocal photonic functionality to a variety of integrated photonic devices.

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in \url{http://medicaldecathlon.com/}. In addition, both data and online evaluation are accessible via \url{www.lits-challenge.com}., Comment: Patrick Bilic, Patrick Christ, Hongwei Bran Li, and Eugene Vorontsov made equal contributions to this work. Published in Medical Image Analysis