ArXiv最新文献

{"title":"Learning temporal relationships between symbols with Laplace Neural Manifolds.","authors":"Marc W Howard, Zahra Gh Esfahani, Bao Le, Per B Sederberg","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Firing across populations of neurons in many regions of the mammalian brain maintains a temporal memory, a neural timeline of the recent past. Behavioral results demonstrate that people can both remember the past and anticipate the future over an analogous internal timeline. This paper presents a mathematical framework for building this timeline of the future. We assume that the input to the system is a time series of symbols-sparse tokenized representations of the present-in continuous time. The goal is to record pairwise temporal relationships between symbols over a wide range of time scales. We assume that the brain has access to a temporal memory in the form of the real Laplace transform. Hebbian associations with a diversity of synaptic time scales are formed between the past timeline and the present symbol. The associative memory stores the convolution between the past and the present. Knowing the temporal relationship between the past and the present allows one to infer relationships between the present and the future. With appropriate normalization, this Hebbian associative matrix can store a Laplace successor representation and a Laplace predecessor representation from which measures of temporal contingency can be evaluated. The diversity of synaptic time constants allows for learning of non-stationary statistics as well as joint statistics between triplets of symbols. This framework synthesizes a number of recent neuroscientific findings including results from dopamine neurons in the mesolimbic forebrain.</p>","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9113356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic Genotype-Phenotype Maps Reveal Mutational Robustness of RNA Folding, Spin Glasses, and Quantum Circuits.","authors":"Anna Sappington, Vaibhav Mohanty","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Recent studies of genotype-phenotype (GP) maps have reported universally enhanced phenotypic robustness to genotype mutations, a feature essential to evolution. Virtually all of these studies make a simplifying assumption that each genotype-represented as a sequence-maps deterministically to a single phenotype, such as a discrete structure. Here, we introduce probabilistic genotype-phenotype (PrGP) maps, where each genotype maps to a vector of phenotype probabilities, as a more realistic and universal language for investigating robustness in a variety of physical, biological, and computational systems. We study three model systems to show that PrGP maps offer a generalized framework which can handle uncertainty emerging from various physical sources: (1) thermal fluctuation in RNA folding, (2) external field disorder in spin glass ground state finding, and (3) superposition and entanglement in quantum circuits, which are realized experimentally on IBM quantum computers. In all three cases, we observe a novel biphasic robustness scaling which is enhanced relative to random expectation for more frequent phenotypes and approaches random expectation for less frequent phenotypes. We derive an analytical theory for the behavior of PrGP robustness, and we demonstrate that the theory is highly predictive of empirical robustness.</p>","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10592904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability of energy landscape analysis of resting-state functional MRI data.","authors":"Pitambar Khanra, Johan Nakuci, Sarah Muldoon, Takamitsu Watanabe, Naoki Masuda","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Energy landscape analysis is a data-driven method to analyze multidimensional time series, including functional magnetic resonance imaging (fMRI) data. It has been shown to be a useful characterization of fMRI data in health and disease. It fits an Ising model to the data and captures the dynamics of the data as movement of a noisy ball constrained on the energy landscape derived from the estimated Ising model. In the present study, we examine test-retest reliability of the energy landscape analysis. To this end, we construct a permutation test that assesses whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e., within-participant reliability) than across different sets of sessions from different participants (i.e., between-participant reliability). We show that the energy landscape analysis has significantly higher within-participant than between-participant test-retest reliability with respect to four commonly used indices. We also show that a variational Bayesian method, which enables us to estimate energy landscapes tailored to each participant, displays comparable test-retest reliability to that using the conventional likelihood maximization method. The proposed methodology paves the way to perform individual-level energy landscape analysis for given data sets with a statistically controlled reliability.</p>","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10143764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos.","authors":"Polina Turishcheva, Paul G Fahey, Michaela Vystrčilová, Laura Hansel, Rachel Froebe, Kayla Ponder, Yongrong Qiu, Konstantin F Willeke, Mohammad Bashiri, Eric Wang, Zhiwei Ding, Andreas S Tolias, Fabian H Sinz, Alexander S Ecker","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input (https://www.sensorium-competition.net/). This competition includes the collection of a new large-scale dataset from the primary visual cortex of ten mice, containing responses from over 78,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.</p>","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/22/e8/nihpp-2305.19654v1.PMC10312815.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9814779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dual benchmarking study of facial forgery and facial forensics","authors":"Minh Tam Pham, T. T. Huynh, Vinh Tong, T. Nguyen, T. Nguyen, Hongzhi Yin, Q. Nguyen","doi":"10.1049/cit2.12362","DOIUrl":"https://doi.org/10.1049/cit2.12362","url":null,"abstract":"In recent years, visual facial forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as deepfake, fake news, defamation or blackmailing of celebrities, impersonation of politicians in political warfare, and the spreading of rumours to attract views. As a result, a rich body of visual forensic techniques has been proposed in an attempt to stop this dangerous trend. However, there is no comprehensive, fair, and unified performance evaluation to enlighten the community on best performing methods. The authors present a systematic benchmark beyond traditional surveys that provides in‐depth insights into facial forgery and facial forensics, grounding on robustness tests such as contrast, brightness, noise, resolution, missing information, and compression. The authors also provide a practical guideline of the benchmarking results, to determine the characteristics of the methods that serve as a comparative reference in this never‐ending war between measures and countermeasures. The authors’ source code is open to the public.","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LinearAlifold: Linear-Time Consensus Structure Prediction for RNA Alignments.","authors":"Apoorv Malik, Liang Zhang, Milan Gautam, Ning Dai, Sizhen Li, He Zhang, David H Mathews, Liang Huang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has many applications including viral diagnostics and therapeutics. However, the most commonly used tool for this task, RNAalifold, is prohibitively slow for long sequences, due to a cubic scaling with the sequence length, taking over a day on 400 SARS-CoV-2 and SARS-related genomes (~30,000nt). We present LinearAlifold, a much faster alternative that scales linearly with both the sequence length and the number of sequences, based on our work LinearFold that folds a single RNA in linear time. Our work is orders of magnitude faster than RNAalifold (0.7 hours on the above 400 genomes, or ~36$times$ speedup) and achieves higher accuracies when compared to a database of known structures. More interestingly, LinearAlifold's prediction on SARS-CoV-2 correlates well with experimentally determined structures, substantially outperforming RNAalifold. Finally, LinearAlifold supports two energy models (Vienna and BL*) and four modes: minimum free energy (MFE), maximum expected accuracy (MEA), ThreshKnot, and stochastic sampling, each of which takes under an hour for hundreds of SARS-CoV variants. Our resource is at: https://github.com/LinearFold/LinearAlifold (code) and http://linearfold.org/linear-alifold (server).</p>","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9258297/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40566128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gene Set Summarization Using Large Language Models.","authors":"Marcin P Joachimiak, J Harry Caufield, Nomi L Harris, Hyeongsik Kim, Christopher J Mungall","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Molecular biologists frequently interpret gene lists derived from high-throughput experiments and computational analysis. This is typically done as a statistical enrichment analysis that measures the over- or under-representation of biological function terms associated with genes or their properties, based on curated assertions from a knowledge base (KB) such as the Gene Ontology (GO). Interpreting gene lists can also be framed as a textual summarization task, enabling Large Language Models (LLMs) to use scientific texts directly and avoid reliance on a KB. TALISMAN (Terminological ArtificiaL Intelligence SuMmarization of Annotation and Narratives) uses generative AI to perform gene set function summarization as a complement to standard enrichment analysis. This method can use different sources of gene functional information: (1) structured text derived from curated ontological KB annotations, (2) ontology-free narrative gene summaries, or (3) direct retrieval from the model. We demonstrate that these methods are able to generate plausible and biologically valid summary GO term lists for an input gene set. However, LLM-based approaches are unable to deliver reliable scores or p-values and often return terms that are not statistically significant. Crucially, in our experiments these methods were rarely able to recapitulate the most precise and informative term from standard enrichment analysis. We also observe minor differences depending on prompt input information, with GO term descriptions leading to higher recall but lower precision. However, newer LLM models perform statistically significantly better than the oldest model across all performance metrics, suggesting that future models may lead to further improvements. Overall, the results are nondeterministic, with minor variations in prompt resulting in radically different term lists, true to the stochastic nature of LLMs. Our results show that at this point, LLM-based methods are unsuitable as a replacement for standard term enrichment analysis, however they may provide summarization benefits for implicit knowledge integration across extant but unstandardized knowledge, for large sets of features, and where the amount of information is difficult for humans to process.</p>","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9622249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint regional uptake quantification of Thorium-227 and Radium-223 using a multiple-energy-window projection-domain quantitative SPECT method.","authors":"Zekun Li, Nadia Benabdallah, Richard Laforest, Richard L Wahl, Daniel L J Thorek, Abhinav K Jha","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Thorium-227-based alpha-particle radiopharmaceutical therapies ({alpha}-RPTs) are being investigated in several clinical and pre-clinical studies. After administration, Thorium-227 decays to Radium-223, another alpha-particle-emitting isotope, which redistributes within the patient. Reliable dose quantification of both Thorium-227 and Radium-223 is clinically important, and SPECT may perform this quantification as these isotopes also emit X- and gamma-ray photons. However, reliable quantification is challenged by the orders-of-magnitude lower activity compared to conventional SPECT, resulting in a very low number of detected counts, the presence of multiple photopeaks, substantial overlap in the emission spectra of these isotopes, and the image-degrading effects in SPECT. To address these issues, we propose a multiple-energy-window projection-domain quantification (MEW-PDQ) method that jointly estimates the regional activity uptake of both Thorium-227 and Radium-223 directly using the SPECT projection from multiple energy windows. We evaluated the method with realistic simulation studies using anthropomorphic digital phantoms, in the context of imaging patients with bone metastases of prostate cancer and treated with Thorium-227-based {alpha}-RPTs. The proposed method yielded reliable (accurate and precise) regional uptake estimates of both isotopes and outperformed state-of-the-art methods across different lesion sizes and contrasts, in a virtual imaging trial, as well as with moderate levels of intra-regional heterogeneous uptake and with moderate inaccuracies in the definitions of the support of various regions. Additionally, we demonstrated the effectiveness of using multiple energy windows and the variance of the estimated uptake using the proposed method approached the Cram'er-Rao-lower-bound-defined theoretical limit.</p>","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9608651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gene activity fully predicts transcriptional bursting dynamics.","authors":"Po-Ta Chen, Michal Levo, Benjamin Zoller, Thomas Gregor","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Transcription commonly occurs in bursts, with alternating productive (ON) and quiescent (OFF) periods, governing mRNA production rates. Yet, how transcription is regulated through bursting dynamics remains unresolved. Here, we conduct real-time measurements of endogenous transcriptional bursting with single-mRNA sensitivity. Leveraging the diverse transcriptional activities in early fly embryos, we uncover stringent relationships between bursting parameters. Specifically, we find that the durations of ON and OFF periods are linked. Regardless of the developmental stage or body-axis position, gene activity levels predict individual alleles' average ON and OFF periods. Lowly transcribing alleles predominantly modulate OFF periods (burst frequency), while highly transcribing alleles primarily tune ON periods (burst size). These relationships persist even under perturbations of cis-regulatory elements or trans-factors and account for bursting dynamics measured in other species. Our results suggest a novel mechanistic constraint governing bursting dynamics rather than a modular control of distinct parameters by distinct regulatory processes.</p>","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10153294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9807592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Brain Tumor Segmentation - Metastases (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI.","authors":"Ahmed W Moawad, Anastasia Janas, Ujjwal Baid, Divya Ramakrishnan, Rachit Saluja, Nader Ashraf, Leon Jekel, Raisa Amiruddin, Maruf Adewole, Jake Albrecht, Udunna Anazodo, Sanjay Aneja, Syed Muhammad Anwar, Timothy Bergquist, Evan Calabrese, Veronica Chiang, Verena Chung, Gian Marco Marco Conte, Farouk Dako, James Eddy, Ivan Ezhov, Ariana Familiar, Keyvan Farahani, Juan Eugenio Iglesias, Zhifan Jiang, Elaine Johanson, Anahita Fathi Kazerooni, Florian Kofler, Kiril Krantchev, Dominic LaBella, Koen Van Leemput, Hongwei Bran Li, Marius George Linguraru, Katherine E Link, Xinyang Liu, Nazanin Maleki, Zeke Meier, Bjoern H Menze, Harrison Moy, Klara Osenberg, Marie Piraud, Zachary Reitman, Russel Takeshi Shinohara, Nourel Hoda Tahon, Ayman Nada, Yuri S Velichko, Chunhao Wang, Benedikt Wiestler, Walter Wiggins, Umber Shafique, Klara Willms, Arman Avesta, Khaled Bousabarah, Satrajit Chakrabarty, Nicolo Gennaro, Wolfgang Holler, Manpreet Kaur, Pamela LaMontagne, MingDe Lin, Jan Lost, Daniel S Marcus, Ryan Maresca, Sarah Merkaj, Ayaman Nada, Gabriel Cassinelli Pedersen, Marc von Reppert, Aristeidis Sotiras, Oleg Teytelboym, Niklas Tillmans, Malte Westerhoff, Ayda Youssef, Devon Godfrey, Scott Floyd, Andreas Rauschecker, Javier Villanueva-Meyer, Irada Pflüger, Jaeyoung Cho, Martin Bendszus, Gianluca Brugnara, Justin Cramer, Gloria J Guzman Perez-Carillo, Derek R Johnson, Anthony Kam, Benjamin Yin Ming Kwan, Lillian Lai, Neil U Lall, Fatima Memon, Satya Narayana Patro, Bojan Petrovic, Tiffany Y So, Gerard Thompson, Lei Wu, E Brooke Schrickel, Anu Bansal, Frederik Barkhof, Cristina Besada, Sammy Chu, Jason Druzgal, Alexandru Dusoi, Luciano Farage, Fabricio Feltrin, Amy Fong, Steve H Fung, R Ian Gray, Ichiro Ikuta, Michael Iv, Alida A Postma, Amit Mahajan, David Joyner, Chase Krumpelman, Laurent Letourneau-Guillon, Christie M Lincoln, Mate E Maros, Elka Miller, Fanny Morón, Esther A Nimchinsky, Ozkan Ozsarlak, Uresh Patel, Saurabh Rohatgi, Atin Saha, Anousheh Sayah, Eric D Schwartz, Robert Shih, Mark S Shiroishi, Juan E Small, Manoj Tanwar, Jewels Valerie, Brent D Weinberg, Matthew L White, Robert Young, Vahe M Zohrabian, Aynur Azizova, Melanie Maria Theresa Brüßeler, Pascal Fehringer, Mohanad Ghonim, Mohamed Ghonim, Athanasios Gkampenis, Abdullah Okar, Luca Pasquini, Yasaman Sharifi, Gagandeep Singh, Nico Sollmann, Theodora Soumala, Mahsa Taherzadeh, Nikolay Yordanov, Philipp Vollmuth, Martha Foltyn-Dumitru, Ajay Malhotra, Aly H Abayazeed, Francesco Dellepiane, Philipp Lohmann, Víctor M Pérez-García, Hesham Elhalawani, Sanaria Al-Rubaiey, Rui Duarte Armindo, Kholod Ashraf, Moamen M Asla, Mohamed Badawy, Jeroen Bisschop, Nima Broomand Lomer, Jan Bukatz, Jim Chen, Petra Cimflova, Felix Corr, Alexis Crawley, Lisa Deptula, Tasneem Elakhdar, Islam H Shawali, Shahriar Faghani, Alexandra Frick, Vaibhav Gulati, Muhammad Ammar Haider, Fátima Hierro, Rasmus Holmboe Dahl, Sarah Maria Jacobs, Kuang-Chun Jim Hsieh, Sedat G Kandemirli, Katharina Kersting, Laura Kida, Sofia Kollia, Ioannis Koukoulithras, Xiao Li, Ahmed Abouelatta, Aya Mansour, Ruxandra-Catrinel Maria-Zamfirescu, Marcela Marsiglia, Yohana Sarahi Mateo-Camacho, Mark McArthur, Olivia McDonnell, Maire McHugh, Mana Moassefi, Samah Mostafa Morsi, Alexander Muntenu, Khanak K Nandolia, Syed Raza Naqvi, Yalda Nikanpour, Mostafa Alnoury, Abdullah Mohamed Aly Nouh, Francesca Pappafava, Markand D Patel, Samantha Petrucci, Eric Rawie, Scott Raymond, Borna Roohani, Sadeq Sabouhi, Laura M Sanchez-Garcia, Zoe Shaked, Pokhraj P Suthar, Talissa Altes, Edvin Isufi, Yaseen Dhermesh, Jaime Gass, Jonathan Thacker, Abdul Rahman Tarabishy, Benjamin Turner, Sebastiano Vacca, George K Vilanilam, Daniel Warren, David Weiss, Klara Willms, Fikadu Worede, Sara Yousry, Wondwossen Lerebo, Alejandro Aristizabal, Alexandros Karargyris, Hasan Kassem, Sarthak Pati, Micah Sheller, Spyridon Bakas, Jeffrey D Rudie, Mariam Aboian","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied clinical environments and providing personalized volumetric reports to patients undergoing BM treatment.</p>","PeriodicalId":8425,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10113860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}