Pleural and ascitic cytology is essential for diagnosing metastatic cancer and predicting tumor origin, yet microscopic observation alone often leads to low accuracy and observer variability. Although deep learning shows great potential in pathology, its use in pleural and ascitic cytology remains limited. We present a data-efficient deep learning framework (MAMILE-UNI) that directly detects malignancy in pleural and ascitic effusions from cytology smear or cell block whole slide images (WSIs); in evaluation of 1250 WSIs, MAMILE-UNI achieved high AUROC, the mean of sensitivity and specificity (MeanSS), and accuracy. Furthermore, in identifying the origin of cancer from cytology smears, the method also achieved high accuracy, MeanSS and AUROC. Identifying the origin of cancer from histopathological slide images is equally important, and our method achieved high accuracy, precision, sensitivity, F1 score, specificity, MeanSS and AUROC in evaluation with 1,196 WSIs. Fisher’s exact test validated the model predictions (p < 0.001).

Accurate evaluation of tumor size on follow-up computed tomography (CT) scans is critical for assessing treatment efficacy in cancer patients. However, universal lesion segmentation of CT remains a significant challenge due to the complexity and diversity of chest-abdomen-pelvis imaging. Traditional manual segmentation is time-consuming, labor-intensive, and subject to inter-observer variability due to its reliance on radiologists' expertise. Current AI models are often limited to segmentation of a single type of lesion, lacking the capability to handle multiple lesion types within a single framework. Moreover, the high computational cost to analyze volumetric CT data and model the intricate and diverse lesion patterns makes universal lesion segmentation even harder. To address the above-mentioned issues, we introduce an effective and efficient Deep Ladle-Net for fast universal 3D lesion segmentation on the chest-abdomen-pelvis CT scans to examine 10 types of lesions at once, including abdominal lesions, bone lesions, colon lesions, kidney lesions, liver lesions, lung lesions, mediastinal lesions, pancreas lesions, lung nodules, and lymph node lesions. Our framework was evaluated on a large cohort with 7151 lesions from 11 publicly available 3D CT datasets and two private datasets. The experimental results show that our preliminary model achieved excellent performance, outperforming five state-of-the-art methods and ranking third in the 2024 Universal Lesion Segmentation Challenge (ULS23). With improved data normalization and augmentation, the proposed Deep Ladle-Net obtains overall Segmentation Dice 0.773±0.146 on 10 lesion types in our extended study and is particularly good in abdominal lesions, kidney lesions, mediastinal lesions and lung nodules, achieving Segmentation Dice greater than 0.78. Run-time analysis shows that the proposed framework achieves high computational efficiency and takes less than 2 s per case using a single NVIDIA Geforce RTX4080 GPU card. These findings highlight the potential of the proposed method to streamline radiological workflows, support early detection, and improve clinical decision-making with reliable and reproducible 3D lesion segmentation results.

Tailoring cancer treatment to the primary tumor site is essential for optimal outcomes, yet identifying the source of metastasis remains a significant clinical challenge. The primary tumor location influences not only the choice of treatment but also its delivery method, potential side effects and overall prognosis. Cancer of unknown primary (CUP) persists despite comprehensive diagnostic evaluations. We present the first fast, low-cost deep learning based cancer screening approach, i.e. a new data efficient soft multiclass feature augmentation deep learning, to identify the primary origin of malignant cells directly from whole slide images (WSIs) of cytological smears (CS), cell blocks (CB) or histopathology slides. Our framework was evaluated across three datasets: a multi-center histopathology collection of 69 sites (1,196 WSIs), a CS dataset (260 WSIs), and a CB dataset (129 WSIs). The proposed method achieved excellent accuracy, outperforming four state-of-the-art methods in identifying common metastatic origins. These findings demonstrate the potential of this deep learning tool to advance precision oncology by rapidly guiding clinicians toward appropriate, site-specific cancer treatments.

Cell segmentation is a key topic in medical image analysis with a wide range of applications in the study of diagnosis and prognosis of pathology and cytology. Along with the recent development of generative adversarial networks and transformers, there has been a substantial amount of work aimed at developing cell segmentation approaches using deep learning (DL) models. Inspired by this transition, in this survey, we provide a comprehensive review of the current situation and future technology development in cell instance segmentation by systematically reviewing 198 research papers, covering a broad spectrum of models for instance-level cell segmentation from 2020 to 2024, including convolutional networks, encoder–decoder architectures, recurrent networks, transformers and generative adversarial models. We have examined the loss functions, training strategies, evaluation methods, widely used datasets and quantitative performance of individual methods. A comprehensive summary of the selected seminal works on DL-based cell segmentation with microscopic images is further provided to investigate the effectiveness of methods. We have also performed a comparative analysis on two challenging cell instance segmentation datasets with technical challenges, including unclear cell boundaries, clustered or overlapping cells, variations in cell appearance and sparse or missing annotations, utilizing 18 state-of-the-art DL approaches in cell instance segmentation. Finally, we described the strengths and challenges of the cell instance segmentation models with discussions on future research directions in this area.

The pleura is a serous membrane that surrounds the surface of the lungs. The visceral surface secretes fluid into the serous cavity, while the parietal surface ensures that the fluid is properly absorbed. However, when this balance is disrupted, it leads to the formation of pleural Effusion. The most common malignant pleural effusion (MPE) caused by lung cancer or breast cancer, and benign pleural effusions (BPE) caused by Mycobacterium tuberculosis infection, heart failure, or infections related to pneumonia. Today, with the rapid advancement of treatment protocols, accurately diagnosing MPE has become increasingly important. Although cytology smears and cell blocks examinations of pleural effusion are the clinical gold standards for diagnosing MPE, the diagnostic accuracy of these tools can be affected by certain limitations, such as low sensitivity, diagnostic variability across different regions and significant inter-observer variability, leading to a certain proportion of misdiagnoses. This study presents a deep learning (DL) framework, namely Interpretable Multi-scale Attention DL with Self-Supervised Learning Feature Encoder (IMA-SSL), to identifyMPE or BPE using 194 Cytological smears whole-slide images (WSIs) and 188 cell blocks WSIs. The use of DL on WSIs of pleural effusion allows for preliminary results to be obtained in a short time, giving patients the opportunity for earlier diagnosis and treatment. The experimental results show that the proposed IMA-SSL consistently obtained superior performance and outperformed five state-of-the-art (SOTA) methods in malignancy prediction on both cell block and cytological smear datasets and also in identification of aggressive endometrial cancer (EC) using a public TCGA dataset. Fisher's exact test confirmed a highly significant correlation between the outputs of the proposed model and the slide status in the EC and pleural effusion datasets (p < 0.001), substantiating the model's predictive reliability. The proposed method has the potential for practical clinical application in the foreseeable future. It can directly detect the presence of malignant tumor cells from cost-effective cell blocks and pleural effusion cytology smears and facilitate personalized cancer treatment decisions.

Understanding metabolic adaptations to seasonal fluctuations in ectothermic organisms is challenging, especially in tropical species where physiological responses are more pronounced than in temperate or polar counterparts. Traditional analytical methods often fail to account for the complex metabolic adjustments that are present in these substantial responses, and the high-dimensional characteristics of metabolomic data complicate the interpretation process when using conventional statistical methods. We utilized Non-negative Matrix Factorization (NMF), an unsupervised machine learning algorithm, to analyze monthly serum metabolomics data from tilapia over a year in order to overcome these limits. A deeper analysis using NMF demonstrated that carbohydrates gained prominence during warmer months, as evidenced by consistently elevated weights of glycolysis intermediates in our quantitative analysis. Furthermore, fatty acids remained an important factor in both warm and cold seasons. Amino acids emerged as particularly versatile metabolites, exhibiting adaptability during seasonal transitions. This flexibility suggests their crucial role in coordinating energy-related adaptations and potentially facilitating epigenetic and reproductive responses to changing environments. Serum aspartate composition during the warm-cold transition indicated sex-specific metabolic strategies, as sexual dimorphism was observed in the seasonal utilization of fatty acids and aspartate. Collectively, NMF objectively assesses the metabolic tactics of tropical fish and reveals latent patterns in real-world metabolic dynamics. Consequently, it possesses the potential to facilitate metabolomics-driven species conservation in response to environmental changes.

Mismatch repair deficiency (dMMR), also known as high-grade microsatellite instability (MSI-H), is a well- established biomarker for predicting the immunotherapy response in endometrial cancer (EC) and colorectal cancer (CRC). Tumor mutational burden (TMB) has also emerged as an important quantitative genomic biomarker for assessing the efficacy of immune checkpoint inhibitors. Although next-generation sequencing (NGS) can be used to assess MSI and TMB, the high costs, low sample throughput, and significant DNA requirements make NGS impractical for routine clinical screening. In this study, an interpretable, multi-stage attention deep learning (DL) network is introduced to predict pathological subtypes, MSI, TP53 mutations, and TMB directly from low-cost, routinely used histopathological whole slide images of EC and CRC slides. Experimental results showed that this method consistently outperformed seven state-of-the-art approaches in cancer subtyping and molecular status prediction across EC and CRC datasets. Fisher's Least Significant Difference test confirmed a strong correlation between model predictions and actual molecular statuses (MSI, TP53, and TMB) (p < 0.001). Furthermore, Kaplan-Meier disease-free survival analysis revealed that CRC patients with model-predicted high TMB had significantly longer disease-free survival than those with low TMB (p < 0.05). These findings demonstrate that the proposed DL-based approach holds significant potential for directly predicting immunotherapy-related pathological diagnoses and molecular statuses from routine WSIs, supporting personalized cancer immunotherapy treatment decisions in EC and CRC.

Tumor mutational burden (TMB) is an essential biomarker to predict immunotherapy response. TMB measurement is mainly conducted by whole-exome sequencing, which is extremely costly and difficult to apply widely in clinical practice. In this study, we present the first deep learning (DL) approach to not only identify aggressive and non-aggressive endometrial cancer (EC) but also predict TMB status directly from histopathology slides with evaluation on a large EC cohort from the Cancer Genome Atlas (TCGA) platform. The experimental results show that the proposed method achieved excellent performances in both classification of aggressive and non-aggressive ECs and TMB prediction in the aggressive EC and top-ranked performance in comparison with five SOTA DL methods. Interestingly, K-M disease-free survival (DFS) analysis shows that the proposed method successfully differentiates patients having longer DFS with a significant difference (p=0.013) between the TMB predicted classes in the non-aggressive EC. A run time analysis shows that the proposed method performs high efficiency in the prediction time (0.37 s per slide), making the proposed framework achievable for practical clinical usage. These results highlight the efficacy and efficiency of the proposed model to identify aggressive ECs and further predict the TMB status directly from histopathology slides.

Testing for mismatch repair deficiency (dMMR) and high-grade microsatellite instability (MSI-H) has become an integral part of the routine diagnostic workup for colorectal cancer (CRC) and endometrial cancer (EC) in clinical practice, and using MSI to predict the therapeutic response to immune checkpoint inhibitors (ICIs) has now become a hot focus. Recent reports have shown that patients with dMMR/MSI-H tumors in CRC and EC can benefit from ICIs. So far, MSI is the only widely recognized biomarker that can be used to evaluate the effectiveness of immunotherapy for CRC and EC. The humanized monoclonal anti-PD-1 antibody pembrolizumab is the first ICI to have its clinical activity investigated in patients with dMMR/MSI-H CRC and EC. Immunohistochemical testing for dMMR has recently become a standard testing item in pathology laboratories, and next-generation sequencing is also a powerful method for analyzing MSI. Although the examination of MSI is important for immunotherapy, high cost and time constraints have limited the widespread adoption of MSI testing. Here, we developed an ensemble transformer multiple instance deep learning (DL) system and evaluated the system to predict MSI status using histopathological whole slide images (WSIs) from multiple datasets of Tri-Service General Hospital and the Cancer Genome Atlas in MSI-related EC and CRC. The results demonstrate that the presented system has great potential to predict MSI status directly from H E WSIs in EC and CRC, enabling personalized targeted immunotherapy.

HER2 assessment is necessary for patient selection in anti-HER2 targeted treatment. However, manual assessment of HER2 amplification is time-costly, labor-intensive, highly subjective and error-prone. Challenges in HER2 analysis in fluorescence in situ hybridization (FISH) and dual in situ hybridization (DISH) images include unclear and blurry cell boundaries, large variations in cell shapes and signals, overlapping and clustered cells and sparse label issues with manual annotations only on cells with high confidences, producing subjective assessment scores according to the individual choices on cell selection. To address the above-mentioned issues, we have developed a soft-sampling cascade deep learning model and a signal detection model in quantifying CEN17 and HER2 of cells to assist assessment of HER2 amplification status for patient selection of HER2 targeting therapy to breast cancer. In evaluation with two different kinds of clinical datasets, including a FISH data set and a DISH data set, the proposed method achieves high accuracy, recall and F1-score for both datasets in instance segmentation of HER2 related cells that must contain both CEN17 and HER2 signals. Moreover, the proposed method is demonstrated to significantly outperform seven state of the art recently published deep learning methods, including contour proposal network (CPN), soft label-based FCN (SL-FCN), modified fully convolutional network (M-FCN), bilayer convolutional network (BCNet), SOLOv2, Cascade R-CNN and DeepLabv3+ with three different backbones (p 0.01). Clinically, anti-HER2 therapy can also be applied to gastric cancer patients. We applied the developed model to assist in HER2 DISH amplification assessment for gastric cancer patients, and it also showed promising predictive results (accuracy 97.67 1.46%, precision 96.15 5.82%, respectively).

In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit from immune checkpoint inhibitors. High TMB is characterized by a large number of mutated genes, which encode aberrant tumor neoantigens, and implies a better response to immunotherapy. Hence, a part of EC and CRC patients associated with high TMB may have higher chances to receive immunotherapy. TMB measurement was mainly evaluated by whole-exome sequencing or next-generation sequencing, which was costly and difficult to be widely applied in all clinical cases. Therefore, an effective, efficient, low-cost and easily accessible tool is urgently needed to distinguish the TMB status of EC and CRC patients. In this study, we present a deep learning framework, namely Ensemble Transformer-based Multiple Instance Learning with Self-Supervised Learning Vision Transformer feature encoder (ETMIL-SSLViT), to predict pathological subtype and TMB status directly from the H&E stained whole slide images (WSIs) in EC and CRC patients, which is helpful for both pathological classification and cancer treatment planning. Our framework was evaluated on two different cancer cohorts, including an EC cohort with 918 histopathology WSIs from 529 patients and a CRC cohort with 1495 WSIs from 594 patients from The Cancer Genome Atlas. The experimental results show that the proposed methods achieved excellent performance and outperforming seven state-of-the-art (SOTA) methods in cancer subtype classification and TMB prediction on both cancer datasets. Fisher’s exact test further validated that the associations between the predictions of the proposed models and the actual cancer subtype or TMB status are both extremely strong (p<0.001). These promising findings show the potential of our proposed methods to guide personalized treatment decisions by accurately predicting the EC and CRC subtype and the TMB status for effective immunotherapy planning for EC and CRC patients.

PET/CT is extensively used in imaging malignant tumors because it highlights areas of increased glucose metabolism, indicative of cancerous activity. Accurate 3D lesion segmentation in PET/CT imaging is essential for effective oncological diagnostics and treatment planning. In this study, we developed an advanced 3D residual U-Net model for the Automated Lesion Segmentation in Whole-Body PET/CT - Multitracer Multicenter Generalization (autoPET III) Challenge, which will be held jointly with 2024 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference at Marrakesh, Morocco. Proposed model incorporates a novel sample attention boosting technique to enhance segmentation performance by adjusting the contribution of challenging cases during training, improving generalization across FDG and PSMA tracers. The proposed model outperformed the challenge baseline model in the preliminary test set on the Grand Challenge platform, and our team is currently ranking in the 2nd place among 497 participants worldwide from 53 countries (accessed date: 2024/9/4), with Dice score of 0.8700, False Negative Volume of 19.3969 and False Positive Volume of 1.0857.

Endometrial cancer (EC) diagnosis traditionally relies on tumor morphology and nuclear grade, but personalized therapy demands a deeper understanding of tumor mutational burden (TMB), i.e., a key biomarker for immune checkpoint inhibition and immunotherapy response. Traditional TMB prediction methods, such as sequencing exomes or whole genomes, are costly and often unavailable in clinical settings. We present the first TR-MAMIL deep learning framework to predict TMB status and classify the EC cancer subtype directly from H&E-stained WSIs, enabling effective personalized immunotherapy planning and prognostic refinement of EC patients. Our models were evaluated on a large dataset from The Cancer Genome Atlas. TR-MAMIL performed exceptionally well in classifying aggressive and non-aggressive EC, as well as predicting TMB, outperforming seven state-of-the-art approaches. It also performed well in classifying normal and abnormal p53 mutations in EC using H&E WSIs. Kaplan-Meier analysis further demonstrated TR-MAMIL's ability to differentiate patients with longer survival in the aggressive EC.

Ovarian cancer, predominantly epithelial ovarian cancer (EOC), is a global health concern due to its high mortality rate. Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and disease. Bevacizumab is a humanized monoclonal antibody, which blocks VEGF signaling in cancer, inhibits angiogenesis and causes tumor shrinkage, and has been recently approved by the FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Unfortunately, Bevacizumab may also induce harmful adverse effects, such as hypertension, bleeding, arterial thromboembolism, poor wound healing and gastrointestinal perforation. Given the expensive cost and unwanted toxicities, there is an urgent need for predictive methods to identify who could benefit from bevacizumab. Of the 18 (approved) requests from 5 countries, 6 teams using 284 whole section WSIs for training to develop fully automated systems submitted their predictions on a test set of 180 tissue core images, with the corresponding ground truth labels kept private. This paper summarizes the 5 qualified methods successfully submitted to the international challenge of automated prediction of treatment effectiveness in ovarian cancer using the histopathologic images (ATEC23) held at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023 and evaluates the methods in comparison with 5 state of the art deep learning approaches. This study further assesses the effectiveness of the presented prediction models as indicators for patient selection utilizing both Cox proportional hazards analysis and Kaplan-Meier survival analysis. A robust and cost-effective deep learning pipeline for digital histopathology tasks has become a necessity within the context of the medical community. This challenge highlights the limitations of current MIL methods, particularly within the context of prognosis- based classification tasks, and the importance of DCNNs like inception that has nonlinear convolutional modules at various resolutions to facilitate processing the data in multiple resolutions, which is a key feature required for pathology related prediction tasks. This further suggests the use of feature reuse at various scales to improve models for future research directions. In particular, this paper releases the labels of the testing set and provides applications for future research directions in precision oncology to predict ovarian cancer treatment effectiveness and facilitate patient selection via histopathological images.

Localization of the craniofacial landmarks from lateral cephalograms is a fundamental task in cephalometric analysis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Cephalometric Landmark Detection (CL-Detection)" dataset, which is the largest publicly available and comprehensive dataset for cephalometric landmark detection. This multi-center and multi-vendor dataset includes 600 lateral X-ray images with 38 landmarks acquired with different equipment from three medical centers. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go for cephalometric landmark detection. Following the 2023 MICCAI CL-Detection Challenge, we report the results of the top ten research groups using deep learning methods. Results show that the best methods closely approximate the expert analysis, achieving a mean detection rate of 75.719 error of 1.518 mm. While there is room for improvement, these findings undeniably open the door to highly accurate and fully automatic location of craniofacial landmarks. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for the community to benchmark future algorithm developments at https://cl-detection2023.grand-challenge.org/.

Molecular classification, particularly microsatellite instability-high (MSI-H), has gained attention for immunotherapy in endometrial cancer (EC). MSI-H is associated with DNA mismatch repair defects and is a crucial treatment predictor. The NCCN guidelines recommend pembrolizumab and nivolumab for advanced or recurrent MSI-H/mismatch repair deficient (dMMR) EC. However, evaluating MSI in all cases is impractical due to time and cost constraints. To overcome this challenge, we present an effective and efficient deep learning-based model designed to accurately and rapidly assess MSI status of EC using H&E-stained whole slide images. Our framework was evaluated on a comprehensive dataset of gigapixel histopathology images of 529 patients from the Cancer Genome Atlas (TCGA). The experimental results have shown that the proposed method achieved excellent performances in assessing MSI status, obtaining remarkably high results with 96%, 94%, 93% and 100% for endometrioid carcinoma G1G2, respectively, and 87%, 84%, 81% and 94% for endometrioid carcinoma G3, in terms of F-measure, accuracy, precision and sensitivity, respectively. Furthermore, the proposed deep learning framework outperforms four state-of-the-art benchmarked methods by a significant margin (p < 0.001) in terms of accuracy, precision, sensitivity and F-measure, respectively. Additionally, a run time analysis demonstrates that the proposed method achieves excellent quantitative results with high efficiency in AI inference time (1.03 seconds per slide), making the proposed framework viable for practical clinical usage. These results highlight the efficacy and efficiency of the proposed model to assess MSI status of EC directly from histopathological slides.

Overexpression of human epidermal growth factor receptor 2 (HER2/ERBB2) is identified as a prognostic marker in metastatic breast cancer and a predictor to determine the effects of ERBB2-targeted drugs. Accurate ERBB2 testing is essential in determining the optimal treatment for metastatic breast cancer patients. Brightfield dual in situ hybridization (DISH) was recently authorized by the United States Food and Drug Administration for the assessment of ERRB2 overexpression, which however is a challenging task due to a variety of reasons. Firstly, the presence of touching clustered and overlapping cells render it difficult for segmentation of individual HER2 related cells, which must contain both ERBB2 and CEN17 signals. Secondly, the fuzzy cell boundaries make the localization of each HER2 related cell challenging. Thirdly, variation in the appearance of HER2 related cells is large. Fourthly, as manual annotations are usually made on targets with high confidence, causing sparsely labeled data with some unlabeled HER2 related cells defined as background, this will seriously confuse fully supervised AI learning and cause poor model outcomes. To deal with all issues mentioned above, we propose a two-stage weakly supervised deep learning framework for accurate and robust assessment of ERBB2 overexpression. The effectiveness and robustness of the proposed deep learning framework is evaluated on two DISH datasets acquired at two different magnifications. The experimental results demonstrate that the proposed deep learning framework achieves an accuracy of 96.78 ± 1.25, precision of 97.77 ± 3.09, recall of 84.86 ± 5.83 and Dice Index of 90.77 ± 4.1 and an accuracy of 96.43 ± 2.67, precision of 97.82 ± 3.99, recall of 87.14 ± 10.17 and Dice Index of 91.87 ± 6.51 for segmentation of ERBB2 overexpression on the two experimental datasets, respectively. Furthermore, the proposed deep learning framework outperforms 15 state-of-the-art benchmarked methods by a significant margin (P<0.05) with respect to IoU on both datasets.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings. This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.

Advances in computation pathology have continued at an impressive pace in recent years [...]