Selected annotated instance segmentation sub-volumes from a large scale CT data-set of a historic aircraft.

The Me 163 was a Second World War fighter airplane and is currently displayed in the Deutsches Museum in Munich, Germany. A complete computed tomography (CT) scan was obtained using a large scale industrial CT scanner to gain insights into its history, design, and state of preservation. The CT data enables visual examination of the airplane's structural details across multiple scales, from the entire fuselage to individual sprockets and rivets. However, further processing requires instance segmentation of the CT data-set. Currently, there are no adequate computer-assisted tools for automated or semi-automated segmentation of such large scale CT airplane data. As a first step, an interactive data annotation process has been established. So far, seven 512 × 512 × 512 voxel sub-volumes of the Me 163 airplane have been annotated, which can potentially be used for various applications in digital heritage, non-destructive testing, or machine learning. This work describes the data acquisition process, outlines the interactive segmentation and post-processing, and discusses the challenges associated with interpreting and handling the annotated data.

Cost-effective 3D scanning and printing technologies for outer ear reconstruction: current status.

Current 3D scanning and printing technologies offer not only state-of-the-art developments in the field of medical imaging and bio-engineering, but also cost and time effective solutions for surgical reconstruction procedures. Besides tissue engineering, where living cells are used, bio-compatible polymers or synthetic resin can be applied. The combination of 3D handheld scanning devices or volumetric imaging, (open-source) image processing packages, and 3D printers form a complete workflow chain that is capable of effective rapid prototyping of outer ear replicas. This paper reviews current possibilities and latest use cases for 3D-scanning, data processing and printing of outer ear replicas with a focus on low-cost solutions for rehabilitation engineering.

3D segmentation of plant root systems using spatial pyramid pooling and locally adaptive field-of-view inference.

Background: The non-invasive 3D-imaging and successive 3D-segmentation of plant root systems has gained interest within fundamental plant research and selectively breeding resilient crops. Currently the state of the art consists of computed tomography (CT) scans and reconstruction followed by an adequate 3D-segmentation process. Challenge: Generating an exact 3D-segmentation of the roots becomes challenging due to inhomogeneous soil composition, as well as high scale variance in the root structures themselves. Approach: (1) We address the challenge by combining deep convolutional neural networks (DCNNs) with a weakly supervised learning paradigm. Furthermore, (2) we apply a spatial pyramid pooling (SPP) layer to cope with the scale variance of roots. (3) We generate a fine-tuned training data set with a specialized sub-labeling technique. (4) Finally, to yield fast and high-quality segmentations, we propose a specialized iterative inference algorithm, which locally adapts the field of view (FoV) for the network. Experiments: We compare our segmentation results against an analytical reference algorithm for root segmentation (RootForce) on a set of roots from Cassava plants and show qualitatively that an increased amount of root voxels and root branches can be segmented. Results: Our findings show that with the proposed DCNN approach combined with the dynamic inference, much more, and especially fine, root structures can be detected than with a classical analytical reference method. Conclusion: We show that the application of the proposed DCNN approach leads to better and more robust root segmentation, especially for very small and thin roots.

Mitosis domain generalization in histopathology images - The MIDOG challenge.

The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an F1 score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task.

"Chamber #8" - a holistic approach of high-throughput non-destructive assessment of plant roots.

Introduction: In the past years, it has been observed that the breeding of plants has become more challenging, as the visible difference in phenotypic data is much smaller than decades ago. With the ongoing climate change, it is necessary to breed crops that can cope with shifting climatic conditions. To select good breeding candidates for the future, phenotypic experiments can be conducted under climate-controlled conditions. Above-ground traits can be assessed with different optical sensors, but for the root growth, access to non-destructively measured traits is much more challenging. Even though MRI or CT imaging techniques have been established in the past years, they rely on an adequate infrastructure for the automatic handling of the pots as well as the controlled climate. Methods: To address both challenges simultaneously, the non-destructive imaging of plant roots combined with a highly automated and standardized mid-throughput approach, we developed a workflow and an integrated scanning facility to study root growth. Our "chamber #8" contains a climate chamber, a material flow control, an irrigation system, an X-ray system, a database for automatic data collection, and post-processing. The goals of this approach are to reduce the human interaction with the various components of the facility to a minimum on one hand, and to automate and standardize the complete process from plant care via measurements to root trait calculation on the other. The user receives standardized phenotypic traits and properties that were collected objectively. Results: The proposed holistic approach allows us to study root growth of plants in a field-like substrate non-destructively over a defined period and to calculate phenotypic traits of root architecture. For different crops, genotypic differences can be observed in response to climatic conditions which have already been applied to a wide variety of root structures, such as potatoes, cassava, or corn. Discussion: It enables breeders and scientists non-destructive access to root traits. Additionally, due to the non-destructive nature of X-ray computed tomography, the analysis of time series for root growing experiments is possible and enables the observation of kinetic traits. Furthermore, using this automation scheme for simultaneously controlled plant breeding and non-destructive testing reduces the involvement of human resources.

Electromagnetic tool for the endoscopic creation of colon anastomoses-development and feasibility assessment of a novel anastomosis compression implant approach.

BACKGROUND: Colorectal anastomoses are among the most commonly performed interventions in abdominal surgery, while associated patient trauma is still high. Most recent trends of endoscopic anastomosis devices integrate magnetic components to overcome the challenges of minimally invasive surgery. However, the mutual attraction between magnetic implant halves may increase the risk of inadvertently pinching healthy structures. Thus, we present a novel anastomosis device to improve system controllability and flexibility. METHODS: A magnetic implant and an applicator with electromagnetic control units were developed. The interaction of magnetic implants with the electromagnets bears particular challenges with respect to the force-related dimensioning. Here, attraction forces must be overcome by the electromagnet actuation to detach the implant, while the attraction force between the implant halves must be sufficient to ensure a stable connection. Thus, respective forces were measured and the detachment process was reproducibly investigated. Patient hazards, associated with resistance-related heating of the coils were investigated. RESULTS: Anastomosis formation was reproducibly successful for an implant, with an attraction force of 1.53 [Formula: see text], resulting in a compression pressure of [Formula: see text]. The implant was reproducibly detachable from the applicator at the anastomosis site. Coils heated up to a maximum temperature of [Formula: see text]. Furthermore, we were able to establish a neat reconnection of intestinal bowel endings using our implant. DISCUSSION: As we achieved nearly equal compression forces with our implant as other magnetic anastomosis systems did (Magnamosis™: 1.48 N), we concluded that our approach provides sufficient holding strength to counteract the forces acting immediately postoperatively, which would eventually lead to an undesired slipping of the implant halves during the healing phase. Based on heat transfer investigations, preventive design specifications were derived, revealing that the wall thickness of a polymeric isolation is determined rather by stability considerations, than by heat shielding requirements.

[Presentation and evaluation of the haptic-visual ear surgery training system "HaptiVisT"]. / Vorstellung und Evaluation des haptisch-visuellen, ohrchirurgischen Trainingssystems "HaptiVisT".

BACKGROUND: Operations on the temporal bone are a special challenge for ENT surgeons. The aim of the BMBF-funded project was to develop a realistic training system for ear operations in the form of a "serious game". METHODS: The presented prototype of the HaptiVisT system functions as a training system for ear surgeries with visual feedback through a glasses-free 3D monitor and feedback by means of a haptic arm simulating the drill. A variety of training options is guaranteed by three available surgical procedures (antrotomy, mastoidectomy, posterior tympanotomy). A weighted point system enables the measurability of the training success. Following the technical development of the prototype, a prospective evaluation was carried out by eight ENT physicians and four students regarding "learning content" and "user experience". A standardized questionnaire was used (ordinal scale: 1=very good to 5=very bad). RESULTS: Regarding the learning content, the aspects "strengthening anatomy (mean=1.58)", "training hand-eye coordination (1.67)", "transferability into practice (1.83)", "usefulness for practice (1.33)" yielded good to very good scores. "User experience" also showed good results for the aspects "realism (2.29)", "interaction of haptics and optics (2.33)" and "immersion in the training system (1.89)". The "motivation factor" was very high for all test subjects (1.2). CONCLUSIONS: The training system for ear surgeries "HaptiVisT" offers the possibility of immersive training. Integration into the daily clinical routine and in particular into the medical training to become an ENT specialist therefore seems to make sense.

Fast whole-slide cartography in colon cancer histology using superpixels and CNN classification.

Purpose: Automatic outlining of different tissue types in digitized histological specimen provides a basis for follow-up analyses and can potentially guide subsequent medical decisions. The immense size of whole-slide-images (WSIs), however, poses a challenge in terms of computation time. In this regard, the analysis of nonoverlapping patches outperforms pixelwise segmentation approaches but still leaves room for optimization. Furthermore, the division into patches, regardless of the biological structures they contain, is a drawback due to the loss of local dependencies. Approach: We propose to subdivide the WSI into coherent regions prior to classification by grouping visually similar adjacent pixels into superpixels. Afterward, only a random subset of patches per superpixel is classified and patch labels are combined into a superpixel label. We propose a metric for identifying superpixels with an uncertain classification and evaluate two medical applications, namely tumor area and invasive margin estimation and tumor composition analysis. Results: The algorithm has been developed on 159 hand-annotated WSIs of colon resections and its performance is compared with an analysis without prior segmentation. The algorithm shows an average speed-up of 41% and an increase in accuracy from 93.8% to 95.7%. By assigning a rejection label to uncertain superpixels, we further increase the accuracy by 0.4%. While tumor area estimation shows high concordance to the annotated area, the analysis of tumor composition highlights limitations of our approach. Conclusion: By combining superpixel segmentation and patch classification, we designed a fast and accurate framework for whole-slide cartography that is AI-model agnostic and provides the basis for various medical endpoints.

Panoramic Imaging Assessment of Different Bladder Phantoms - An Evaluation Study.

OBJECTIVE: To evaluate "panoramic image stitching" for cystoscopy, a novel technique to augment a urologist's field of view transoperatively in real-time during a cystoscopic "keyhole" procedure, 3-D bladder phantoms provide a suitable setting. Thus, the objective is the evaluation of different 3-D printed bladder phantoms with respect to their ability to be used for extended experiments of panoramic cystoscopy. MATERIALS AND METHODS: Five bladder phantoms with different geometries, surface textures, stiffness and materials were assessed with rigid 0- and 30-degree lenses and a video-cystoscope regarding suitability for image stitching. For panoramic image generation, we use an established real-time stitching approach successfully applied to retrospective cystoscopy image sequences from real clinical cases. For evaluation of the experiments two quality criteria were defined, namely 'completeness' (describing the internal area of the phantom that can be stitched), and 'extension' (how far does an acquired panorama extend beyond the assumed 'equator' of the phantom). RESULTS: Panoramas of all phantom and endoscope combinations were computed. Using landmarks (south pole, north pole, equator) in the phantoms, maximum extension of the panoramas was assessed. The computed panoramas yield maximum extensions between 270o (0-degree cystoscope) and 330o (video cystoscope). Deformable phantoms yield larger panoramas than the rigid models. CONCLUSION: In can be concluded that the stitching process works quite well with all evaluated phantoms. Nevertheless, a novel phantom would be needed, combing a deformable, elliptical geometry and real-type vascular texture in the inside, that can be filled with water.

Semiautomated 3D Root Segmentation and Evaluation Based on X-Ray CT Imagery.

BACKGROUND: Computed X-ray tomography (CTX) is a high-end nondestructive approach for the visual assessment of root architecture in soil. Nevertheless, in order to evaluate high-resolution CTX data of root architectures, manual segmentation of the depicted root systems from large-scale volume data is currently necessary, which is both time consuming and error prone. The duration of such a segmentation is of importance, especially for time-resolved growth analysis, where several instances of a plant need to be segmented and evaluated. Specifically, in our application, the contrast between soil and root data varies due to different growth stages and watering situations at the time of scanning. Additionally, the root system itself is expanding in length and in the diameter of individual roots. OBJECTIVE: For semiautomated and robust root system segmentation from CTX data, we propose the RootForce approach, which is an extension of Frangi's "multi-scale vesselness" method and integrates a 3D local variance. It allows a precise delineation of roots with diameters down to several µm in pots with varying diameters. Additionally, RootForce is not limited to the segmentation of small below-ground organs, but is also able to handle storage roots with a diameter larger than 40 voxels. RESULTS: Using CTX volume data of full-grown bean plants as well as time-resolved (3D + time) growth studies of cassava plants, RootForce produces similar (and much faster) results compared to manual segmentation of the regarded root architectures. Furthermore, RootForce enables the user to obtain traits not possible to be calculated before, such as total root volume (V root), total root length (L root), root volume over depth, root growth angles (θ min, θ mean, and θ max), root surrounding soil density D soil, or form fraction F. Discussion. The proposed RootForce tool can provide a higher efficiency for the semiautomatic high-throughput assessment of the root architectures of different types of plants from large-scale CTX. Furthermore, for all datasets within a growth experiment, only a single set of parameters is needed. Thus, the proposed tool can be used for a wide range of growth experiments in the field of plant phenotyping.

Artificial Intelligence-Based Polyp Detection in Colonoscopy: Where Have We Been, Where Do We Stand, and Where Are We Headed?

BACKGROUND: In the past, image-based computer-assisted diagnosis and detection systems have been driven mainly from the field of radiology, and more specifically mammography. Nevertheless, with the availability of large image data collections (known as the "Big Data" phenomenon) in correlation with developments from the domain of artificial intelligence (AI) and particularly so-called deep convolutional neural networks, computer-assisted detection of adenomas and polyps in real-time during screening colonoscopy has become feasible. SUMMARY: With respect to these developments, the scope of this contribution is to provide a brief overview about the evolution of AI-based detection of adenomas and polyps during colonoscopy of the past 35 years, starting with the age of "handcrafted geometrical features" together with simple classification schemes, over the development and use of "texture-based features" and machine learning approaches, and ending with current developments in the field of deep learning using convolutional neural networks. In parallel, the need and necessity of large-scale clinical data will be discussed in order to develop such methods, up to commercially available AI products for automated detection of polyps (adenoma and benign neoplastic lesions). Finally, a short view into the future is made regarding further possibilities of AI methods within colonoscopy. KEY MESSAGES: Research of image-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.

Force-feedback assisted and virtual fixtures based K-wire drilling simulation.

One common method to fix fractures of the human hand after an accident is an osteosynthesis with Kirschner wires (K-wires) to stabilize the bone fragments. The insertion of K-wires is a delicate minimally invasive surgery, because surgeons operate almost without a sight. Since realistic training methods are time consuming, costly and insufficient, a virtual-reality (VR) based training system for the placement of K-wires was developed. As part of this, the current work deals with the real-time bone drilling simulation using a haptic force-feedback device. To simulate the drilling, we introduce a virtual fixture based force-feedback drilling approach. By decomposition of the drilling task into individual phases, each phase can be handled individually to perfectly control the drilling procedure. We report about the related finite state machine (FSM), describe the haptic feedback of each state and explain, how to avoid jerking of the haptic force-feedback during state transition. The usage of the virtual fixture approach results in a good haptic performance and a stable drilling behavior. This was confirmed by 26 expert surgeons, who evaluated the virtual drilling on the simulator and rated it as very realistic. To make the system even more convincing, we determined real drilling feed rates through experimental pig bone drilling and transferred them to our system. Due to a constant simulation thread we can guarantee a precise drilling motion. Virtual fixtures based force-feedback calculation is able to simulate force-feedback assisted bone drilling with high quality and, thus, will have a great potential in developing medical applications.

Co-staining of microRNAs and their target proteins by miRNA in situ hybridization and immunohistofluorescence on prostate cancer tissue microarrays.

The co-expression of miRNAs and their target proteins was studied on tissue microarrays from different prostate cancer (PCa) patients. PCa of primary Gleason pattern 4 (GP4), lymph node metastases of GP4, distant metastases, and normal tissue from the transitional and peripheral zones were co-stained by fluorescent miRNA in situ hybridization (miRisH) and protein immunohistofluorescence (IHF). The miRNAs and corresponding target proteins include the pairs miR-145/ERG, miR-143/uPAR, and miR-375/SEC23A. The fluorescence-stained and scanned tissue microarrays (TMAs) were evaluated by experienced uropathologists. The pair miR-145/ERG showed an exclusive staining for miR-145 in the nuclei of stromal cells, both in tumor and normal tissue, and for ERG in the cytoplasm with/without co-expression in the nucleus of tumor cells. The pair miR-143/uPAR revealed a clear distinction between miR-143 in the nuclei of stromal cells and uPAR staining in the cytoplasm of tumor cells. Metastases (lymph node and distant) however, showed tumor cells with cytoplasmic staining for miR-143/uPAR. In normal tissues, beside the nuclei of the stroma cells, gland cells could also express miR-143 and uPAR in the cytoplasm. miR-375 showed particular staining in the nucleoli of GP4 and metastatic samples, suggesting that nucleoli play a special role in sequestering proteins and miRNAs. Combined miRisH/IHF allows for the study of miRNA expression patterns and their target proteins at the single-cell level.

Automated polyp detection in the colorectum: a prospective study (with videos).

BACKGROUND AND AIMS: Adenoma detection is a highly personalized task that differs markedly among endoscopists. Technical advances are therefore desirable for the improvement of the adenoma detection rate (ADR). An automated computer-driven technology would offer the chance to objectively assess the presence of colorectal polyps during colonoscopy. We present here the application of a real-time automated polyp detection software (APDS) under routine colonoscopy conditions. METHODS: This was a prospective study at a university hospital in Germany. A prototype of a novel APDS ("KoloPol," Fraunhofer IIS, Erlangen, Germany) was used for automated image-based polyp detection. The software functions by highlighting structures of possible polyp lesions in a color-coded manner during real-time colonoscopy procedures. Testing the feasibility of APDS deployment under real-time conditions was the primary goal of the study. APDS polyp detection rates (PDRs) were defined as secondary endpoints provided that endoscopists' detection served as criterion standard. RESULTS: The APDS was applied in 55 routine colonoscopies without the occurrence of any clinically relevant adverse events. Endoscopists' PDRs and ADRs were 56.4% and 30.9%, respectively. The PDRs and ADRs of the APDS were 50.9% and 29.1%, respectively. The APDS detected 55 of 73 polyps (75.3%). Smaller polyp size and flat polyp morphology were correlated with insufficient polyp detection by the APDS. CONCLUSION: Computer-assisted automated low-delay polyp detection is feasible during real-time colonoscopy. Efforts should be undertaken to improve the APDS with respect to smaller and flat shaped polyps. (Clinical trial registration number: NCT02838888.).

IFN-γ-response mediator GBP-1 represses human cell proliferation by inhibiting the Hippo signaling transcription factor TEAD.

Interferon-gamma (IFN-γ) is a pleiotropic cytokine that exerts important functions in inflammation, infectious diseases, and cancer. The large GTPase human guanylate-binding protein 1 (GBP-1) is among the most strongly IFN-γ-induced cellular proteins. Previously, it has been shown that GBP-1 mediates manifold cellular responses to IFN-γ including the inhibition of proliferation, spreading, migration, and invasion and through this exerts anti-tumorigenic activity. However, the mechanisms of GBP-1 anti-tumorigenic activities remain poorly understood. Here, we elucidated the molecular mechanism of the human GBP-1-mediated suppression of proliferation by demonstrating for the first time a cross-talk between the anti-tumorigenic IFN-γ and Hippo pathways. The α9-helix of GBP-1 was found to be sufficient to inhibit proliferation. Protein-binding and molecular modeling studies revealed that the α9-helix binds to the DNA-binding domain of the Hippo signaling transcription factor TEA domain protein (TEAD) mediated by the 376VDHLFQK382 sequence at the N-terminus of the GBP-1-α9-helix. Mutation of this sequence resulted in abrogation of both TEAD interaction and suppression of proliferation. Further on, the interaction caused inhibition of TEAD transcriptional activity associated with the down-regulation of TEAD-target genes. In agreement with these results, IFN-γ treatment of the cells also impaired TEAD activity, and this effect was abrogated by siRNA-mediated inhibition of GBP-1 expression. Altogether, this demonstrated that the α9-helix is the proliferation inhibitory domain of GBP-1, which acts independent of the GTPase activity through the inhibition of the Hippo transcription factor TEAD in mediating the anti-proliferative cell response to IFN-γ.

Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound.

BACKGROUND: Tumors in radiologically dense breast were overlooked on mammograms more often than tumors in low-density breasts. A fast reproducible and automated method of assessing percentage mammographic density (PMD) would be desirable to support decisions whether ultrasonography should be provided for women in addition to mammography in diagnostic mammography units. PMD assessment has still not been included in clinical routine work, as there are issues of interobserver variability and the procedure is quite time consuming. This study investigated whether fully automatically generated texture features of mammograms can replace time-consuming semi-automatic PMD assessment to predict a patient's risk of having an invasive breast tumor that is visible on ultrasound but masked on mammography (mammography failure). METHODS: This observational study included 1334 women with invasive breast cancer treated at a hospital-based diagnostic mammography unit. Ultrasound was available for the entire cohort as part of routine diagnosis. Computer-based threshold PMD assessments ("observed PMD") were carried out and 363 texture features were obtained from each mammogram. Several variable selection and regression techniques (univariate selection, lasso, boosting, random forest) were applied to predict PMD from the texture features. The predicted PMD values were each used as new predictor for masking in logistic regression models together with clinical predictors. These four logistic regression models with predicted PMD were compared among themselves and with a logistic regression model with observed PMD. The most accurate masking prediction was determined by cross-validation. RESULTS: About 120 of the 363 texture features were selected for predicting PMD. Density predictions with boosting were the best substitute for observed PMD to predict masking. Overall, the corresponding logistic regression model performed better (cross-validated AUC, 0.747) than one without mammographic density (0.734), but less well than the one with the observed PMD (0.753). However, in patients with an assigned mammography failure risk >10%, covering about half of all masked tumors, the boosting-based model performed at least as accurately as the original PMD model. CONCLUSION: Automatically generated texture features can replace semi-automatically determined PMD in a prediction model for mammography failure, such that more than 50% of masked tumors could be discovered.

Analysis of Corynebacterium diphtheriae macrophage interaction: Dispensability of corynomycolic acids for inhibition of phagolysosome maturation and identification of a new gene involved in synthesis of the corynomycolic acid layer.

Corynebacterium diphtheriae is the causative agent of diphtheria, a toxin mediated disease of upper respiratory tract, which can be fatal. As a member of the CMNR group, C. diphtheriae is closely related to members of the genera Mycobacterium, Nocardia and Rhodococcus. Almost all members of these genera comprise an outer membrane layer of mycolic acids, which is assumed to influence host-pathogen interactions. In this study, three different C. diphtheriae strains were investigated in respect to their interaction with phagocytic murine and human cells and the invertebrate infection model Caenorhabditis elegans. Our results indicate that C. diphtheriae is able to delay phagolysosome maturation after internalization in murine and human cell lines. This effect is independent of the presence of mycolic acids, as one of the strains lacked corynomycolates. In addition, analyses of NF-κB induction revealed a mycolate-independent mechanism and hint to detrimental effects of the different strains tested on the phagocytic cells. Bioinformatics analyses carried out to elucidate the reason for the lack of mycolates in one of the strains led to the identification of a new gene involved in mycomembrane formation in C. diphtheriae.

Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms.

BACKGROUND: Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines. RESULTS: We present a new approach to simulate fluorescent cell micrographs that provides an objective ground truth for the validation of cell segmentation methods. The cell simulation was evaluated twofold: (1) An expert observer study shows that the proposed approach generates realistic fluorescent cell micrograph simulations. (2) An automated segmentation pipeline on the simulated fluorescent cell micrographs reproduces segmentation performances of that pipeline on real fluorescent cell micrographs. CONCLUSION: The proposed simulation approach produces realistic fluorescent cell micrographs with corresponding ground truth. The simulated data is suited to evaluate image segmentation pipelines more efficiently and reproducibly than it is possible on manually annotated real micrographs.

Digital Mapping of the Urinary Bladder: Potential for Standardized Cystoscopy Reports.

OBJECTIVE: To develop a standardized digital reporting tool for cystoscopy of the urinary bladder using panoramic imaging. MATERIALS AND METHODS: An image processing and stitching software (Endorama) was developed to generate panoramic images from cystoscopy data. In a processing phase, algorithms were modulated and refined by reference to cystoscopy sequences (n = 30). Subsequently, standard systematic cystoscopies (n = 12) were recorded in patients undergoing transurethral resection of a bladder tumor to create panoramic images. RESULTS: All sequences were applicable for the development and refinements of the software. Processing increasingly allowed the creation of images illustrating large parts of the bladder and relevant anatomic landmarks in different locations. The pathway covered by the endoscope during the intervention was illustrated as a route in the respective digital image. During the application phase, panoramic images were successfully created in 10 out of 12 cases. The resolution of the images was 4096 × 2048 pixels and the images required a median digital memory of 3.9 MB (3.4-5.7). The panoramic images illustrated 22 relevant findings of which 7 were papillary tumors. CONCLUSION: High-quality digital panoramic maps of the urinary bladder were created using specifically processed data of videocystoscopy. In this preliminary series, relevant findings were illustrated in the respective image. Our tool may help improve standardization of cystoscopy reports and reduce interobserver variability.

Semi-automated delineation of breast cancer tumors and subsequent materialization using three-dimensional printing (rapid prototyping).

OBJECTIVE: Three-dimensional (3D) printing has become widely available, and a few cases of its use in clinical practice have been described. The aim of this study was to explore facilities for the semi-automated delineation of breast cancer tumors and to assess the feasibility of 3D printing of breast cancer tumors. METHODS: In a case series of five patients, different 3D imaging methods-magnetic resonance imaging (MRI), digital breast tomosynthesis (DBT), and 3D ultrasound-were used to capture 3D data for breast cancer tumors. The volumes of the breast tumors were calculated to assess the comparability of the breast tumor models, and the MRI information was used to render models on a commercially available 3D printer to materialize the tumors. RESULTS: The tumor volumes calculated from the different 3D methods appeared to be comparable. Tumor models with volumes between 325 mm3 and 7,770 mm3 were printed and compared with the models rendered from MRI. The materialization of the tumors reflected the computer models of them. CONCLUSION: 3D printing (rapid prototyping) appears to be feasible. Scenarios for the clinical use of the technology might include presenting the model to the surgeon to provide a better understanding of the tumor's spatial characteristics in the breast, in order to improve decision-making in relation to neoadjuvant chemotherapy or surgical approaches. J. Surg. Oncol. 2017;115:238-242. © 2016 Wiley Periodicals, Inc.