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Open problems in Medical image segmentation

Lack of benchmarks aligning with real clinical use2 papers raise it · 2 address it
Current evaluations do not reflect how clinicians actually use AI, risking misjudgment of model readiness and safety.
- Clinician use of language models diverges from how the models are evaluatedraises · addresses
- MoveBench: A Rubric-Based Benchmark for Evaluating Large Language Models in Sports and Health Coachingraises · addresses
Handling incidental or irrelevant information in clinical data1 paper raises it · 0 address it
Models may incorporate or misattribute non-clinical content, leading to unsafe or inaccurate outputs.
- Large language models are vulnerable to incidental information in clinical documentation and reasoningraises
Temporal modeling of multimodal clinical trajectories1 paper raises it · 1 addresses it
Accurate risk prediction requires integrating irregular, heterogeneous data over time, which current methods underaddress.
- TRACE: Tracking Multimodal Clinical Trajectories for Inpatient Risk Predictionraises · addresses
Privacy-preserving learning from deployment experience1 paper raises it · 1 addresses it
Frozen models cannot adapt to new clinical evidence without retraining, which is often impractical or unsafe.
- Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AIraises · addresses
Gigapixel whole-slide image generation and segmentation1 paper raises it · 1 addresses it
Existing generative models operate at patch scale, limiting their utility for slide-level tasks like diagnosis.
- BigCell: Generating Gigapixel Whole-Slide Imagesraises · addresses

Named by an AI from these papers' abstracts.

Open problems

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Topics in this field

medical image segmentation · tumor segmentation · brain mri328 papers35 this week
clinical reasoning · question answering · large language models279 papers34 this week
pathology foundation models · instance learning · multiple instance learning178 papers21 this week
health records · cohorts · electronic health records134 papers16 this week
radiology report generation · radiology reports · medical vision language71 papers9 this week
surgical video generation · surgical videos · surgical video55 papers8 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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Recent papers

All papers in Research

arXiv cs.CVSep 29
Multi-task learning for the automatic grading of enlarged perivascular space burden using MRI
Jesse Phitidis, William N. Whiteley, Joanna M. Wardlaw, Miguel O. Bernabeu, Yajun Cheng +9
Enlarged perivascular spaces (PVS) visible in brain magnetic resonance imaging (MRI) are increasingly thought to be linked to poor brain health. PVS are elongated structures of less than 3 mm in diameter and can be numerous. To reflect the incidence of PVS, radiologists visually score their burden following a clinical grading scale - a task that would benefit from automation to accelerate analyses and overcome the influence of inter-observer differences. We developed and evaluated methods for training machine learning models to score PVS incidence in the basal ganglia (BG) and centrum semiovale (CSO) leveraging the Potters/Wardlaw scale. The novelty in our work lies in the use of imperfect, semi-automatically generated "silver-standard" PVS segmentation masks during training, in addition to PVS radiological scores. We comparatively evaluated a conditional convolutional neural network (CNN) which accepts PVS masks as an extra input channel, a multi-task CNN which performs both PVS segmentation and scoring, and a logistic regression model which utilises features derived from PVS masks to predict PVS scores. Multi-task learning was the most effective method, achieving a mean average…
PDF arXiv
arXiv cs.CVSep 29
Anatomy-Aware Prediction of Bronchoscopic Accessibility from 3D CT
Linkai Peng, Cuiling Sun, Bin Wang, Jamie Rowell, Catherine Gao +8
Pre-operative planning for bronchoscopy is critical for the diagnosis of lung lesions. Current accessibility assessment relies on subjective manual inspection of CT scans, which is time-consuming and prone to inter-observer variability. In this paper, we formalize bronchoscopy accessibility prediction as a novel supervised learning task and present the first end-to-end framework to address it. We propose an Anatomy-Aware Mixture-of-Experts (MoE) model that integrates specialized modules: a CT Expert for local morphological features, a Lobe Expert for anatomical priors, and a Path Geometry Expert that encodes the sequential constraints of the bronchial tree. To support this task, we curated the first clinical dataset of 438 cases with pre-operative CT scans and documented procedural outcomes. Experimental results demonstrate that our method achieves an AUROC of 0.8052, significantly outperforming both state-of-the-art baselines and experienced human experts. This work establishes a new benchmark for computer-aided interventional planning in pulmonary medicine. Our data and code will be publicly available at https://nubagcilab.github.io/BronchoAccess/.
PDF arXiv
arXiv cs.CVSep 29
PCaPaint: Prostate Cancer Inpainting by Mitigating Shortcut Learning
Levente Lippenszky, Hongxu Yang, Marcell Dömötör, Krisztian Koos, László Ruskó
The development of AI systems for tumor-specific applications is limited by the scarcity of labeled data. Synthetic tumor inpainting offers a promising approach but faces challenges for prostate cancer MRI which contains high-resolution multi-sequence data. Although methods leveraging latent diffusion models (LDMs) enable large-volume synthesis, they are prone to shortcut learning, simply reproducing the condition image created by masking the lesion region. In this work, we introduce PCaPaint, a prostate cancer inpainting method based on LDMs that explicitly addresses this failure mode. To overcome shortcut learning that compromises synthetic tumor texture, we propose a simple yet efficient conditioning strategy in which the condition image is filled with Gaussian noise, and we provide theoretical justification. In addition, we propose a novel training objective for LDM that emphasizes the error within the lesion region. Furthermore, we introduce a multi-sequence latent design, in which T2w scans and DWI&ADC scans are compressed using two separate autoencoders to preserve their distinct frequency characteristics. Extensive experiments demonstrate that the generated synthetic data…
PDF arXiv
arXiv cs.CVSep 29
S2T-Unet: A Structure-to-Style Framework for Inter-Modality MRI Translation
Yichao Liu
Inter-modality MRI translation aims to synthesize missing MRI modalities from available acquisitions, reducing the need for additional scanning while preserving clinically relevant anatomical information. However, existing image translation methods often learn intensity mappings without explicitly separating modality-invariant structural information from modality-specific appearance, which may lead to structural information loss or unrealistic image details. In this work, we propose S2T-Unet, a structure-to-style framework that explicitly models these two aspects. Specifically, vector quantization is introduced at the lower-level bottleneck to encode modality-invariant structural information using a learned discrete codebook. At higher levels, a modality transformation module uses decoder features to condition and transform encoder representations toward the target modality, thereby recovering modality-specific intensity and contrast information. Experiments on the IXI multi-contrast MRI dataset across four translation tasks demonstrate that S2T-Unet is comparable or outperform with state-of-art method.
PDF arXiv
arXiv cs.AISep 29
Automated Screw Planning for Reduced Pelvic Fractures Based on Statistical Shape Models and Deep Learning
Yang Gao, Sutuke Yibulayimu, Yanzhen Liu, Zian Zhao, Yudi Sang
Percutaneous iliosacral screw fixation is an important minimally invasive treatment for unstable pelvic fractures. Because the sacroiliac region has complex anatomy and narrow screw corridors, the accuracy and safety of screw placement directly affect surgical outcomes. Accurate and reliable preoperative screw planning is therefore essential to improve surgical success and reduce intraoperative risks. Conventional preoperative planning typically requires surgeons to determine screw trajectories through manual measurements, a labor-intensive process that depends on subjective clinical experience. To address these challenges, we propose a fully automated pipeline for preoperative iliosacral screw planning in patients with pelvic fractures. Using patient-specific three-dimensional anatomy, the pipeline automatically identifies safe screw corridors and generates individualized insertion trajectories to support clinical preoperative planning. We evaluated the proposed pipeline on 200 clinical cases of pelvic fractures. Compared with conventional manual measurements, the safety margin of the safe insertion corridors increased by 2% across the four screw types, the mean planning time…
PDF arXiv
arXiv cs.CVSep 29
You Cannot Recover What Was Never Measured: Quantifying the Information Ceiling of Ultra-Low-Field MRI Super-Resolution
Prathamesh Pradeep Khole, Shreya Handa, Utkarsh Gupta, Razvan Marinescu
Generative super-resolution models can turn portable 64 mT MRI into images that look like 3T scans, and the field evaluates them with PSNR, SSIM, and pixelwise uncertainty, most often on pairs built by synthetically degrading high-field images. Prior work acknowledges that these models hallucinate and that the problem is ill posed, but to our knowledge no study measures how much information about the individual subject the real low-field scan actually contains. We measure it. Using paired 64 mT and 3T scans of the same subjects from three public datasets, and a measurement protocol validated on tests whose correct answer is known in advance, we find that, judged over the whole brain, real 64 mT scans carry structure specific to the individual only down to approximately 3 to 4 mm half-pitch in plane, and coarser still through plane. Standard synthetic degradations preserve subject information roughly 1 mm beyond this ceiling, so models trained and benchmarked on synthetic pairs are evaluated on information that real scanners never record. We then test trained diffusion models and a publicly released external model on real paired acquisitions; 24 trained runs of five architectures…
PDF arXiv

Papers

Topics in this field

medical image segmentation · tumor segmentation · brain mri328 papers35 this week
clinical reasoning · question answering · large language models279 papers34 this week
pathology foundation models · instance learning · multiple instance learning178 papers21 this week
health records · cohorts · electronic health records134 papers16 this week
radiology report generation · radiology reports · medical vision language71 papers9 this week
surgical video generation · surgical videos · surgical video55 papers8 this week

Active contributors · 90 days

Recent papers

All papers in Research

arXiv cs.CVSep 29
Multi-task learning for the automatic grading of enlarged perivascular space burden using MRI
Jesse Phitidis, William N. Whiteley, Joanna M. Wardlaw, Miguel O. Bernabeu, Yajun Cheng +9
Enlarged perivascular spaces (PVS) visible in brain magnetic resonance imaging (MRI) are increasingly thought to be linked to poor brain health. PVS are elongated structures of less than 3 mm in diameter and can be numerous. To reflect the incidence of PVS, radiologists visually score their burden following a clinical grading scale - a task that would benefit from automation to accelerate analyses and overcome the influence of inter-observer differences. We developed and evaluated methods for training machine learning models to score PVS incidence in the basal ganglia (BG) and centrum semiovale (CSO) leveraging the Potters/Wardlaw scale. The novelty in our work lies in the use of imperfect, semi-automatically generated "silver-standard" PVS segmentation masks during training, in addition to PVS radiological scores. We comparatively evaluated a conditional convolutional neural network (CNN) which accepts PVS masks as an extra input channel, a multi-task CNN which performs both PVS segmentation and scoring, and a logistic regression model which utilises features derived from PVS masks to predict PVS scores. Multi-task learning was the most effective method, achieving a mean average…
PDF arXiv
arXiv cs.CVSep 29
Anatomy-Aware Prediction of Bronchoscopic Accessibility from 3D CT
Linkai Peng, Cuiling Sun, Bin Wang, Jamie Rowell, Catherine Gao +8
Pre-operative planning for bronchoscopy is critical for the diagnosis of lung lesions. Current accessibility assessment relies on subjective manual inspection of CT scans, which is time-consuming and prone to inter-observer variability. In this paper, we formalize bronchoscopy accessibility prediction as a novel supervised learning task and present the first end-to-end framework to address it. We propose an Anatomy-Aware Mixture-of-Experts (MoE) model that integrates specialized modules: a CT Expert for local morphological features, a Lobe Expert for anatomical priors, and a Path Geometry Expert that encodes the sequential constraints of the bronchial tree. To support this task, we curated the first clinical dataset of 438 cases with pre-operative CT scans and documented procedural outcomes. Experimental results demonstrate that our method achieves an AUROC of 0.8052, significantly outperforming both state-of-the-art baselines and experienced human experts. This work establishes a new benchmark for computer-aided interventional planning in pulmonary medicine. Our data and code will be publicly available at https://nubagcilab.github.io/BronchoAccess/.
PDF arXiv
arXiv cs.CVSep 29
PCaPaint: Prostate Cancer Inpainting by Mitigating Shortcut Learning
Levente Lippenszky, Hongxu Yang, Marcell Dömötör, Krisztian Koos, László Ruskó
The development of AI systems for tumor-specific applications is limited by the scarcity of labeled data. Synthetic tumor inpainting offers a promising approach but faces challenges for prostate cancer MRI which contains high-resolution multi-sequence data. Although methods leveraging latent diffusion models (LDMs) enable large-volume synthesis, they are prone to shortcut learning, simply reproducing the condition image created by masking the lesion region. In this work, we introduce PCaPaint, a prostate cancer inpainting method based on LDMs that explicitly addresses this failure mode. To overcome shortcut learning that compromises synthetic tumor texture, we propose a simple yet efficient conditioning strategy in which the condition image is filled with Gaussian noise, and we provide theoretical justification. In addition, we propose a novel training objective for LDM that emphasizes the error within the lesion region. Furthermore, we introduce a multi-sequence latent design, in which T2w scans and DWI&ADC scans are compressed using two separate autoencoders to preserve their distinct frequency characteristics. Extensive experiments demonstrate that the generated synthetic data…
PDF arXiv
arXiv cs.CVSep 29
S2T-Unet: A Structure-to-Style Framework for Inter-Modality MRI Translation
Yichao Liu
Inter-modality MRI translation aims to synthesize missing MRI modalities from available acquisitions, reducing the need for additional scanning while preserving clinically relevant anatomical information. However, existing image translation methods often learn intensity mappings without explicitly separating modality-invariant structural information from modality-specific appearance, which may lead to structural information loss or unrealistic image details. In this work, we propose S2T-Unet, a structure-to-style framework that explicitly models these two aspects. Specifically, vector quantization is introduced at the lower-level bottleneck to encode modality-invariant structural information using a learned discrete codebook. At higher levels, a modality transformation module uses decoder features to condition and transform encoder representations toward the target modality, thereby recovering modality-specific intensity and contrast information. Experiments on the IXI multi-contrast MRI dataset across four translation tasks demonstrate that S2T-Unet is comparable or outperform with state-of-art method.
PDF arXiv
arXiv cs.AISep 29
Automated Screw Planning for Reduced Pelvic Fractures Based on Statistical Shape Models and Deep Learning
Yang Gao, Sutuke Yibulayimu, Yanzhen Liu, Zian Zhao, Yudi Sang
Percutaneous iliosacral screw fixation is an important minimally invasive treatment for unstable pelvic fractures. Because the sacroiliac region has complex anatomy and narrow screw corridors, the accuracy and safety of screw placement directly affect surgical outcomes. Accurate and reliable preoperative screw planning is therefore essential to improve surgical success and reduce intraoperative risks. Conventional preoperative planning typically requires surgeons to determine screw trajectories through manual measurements, a labor-intensive process that depends on subjective clinical experience. To address these challenges, we propose a fully automated pipeline for preoperative iliosacral screw planning in patients with pelvic fractures. Using patient-specific three-dimensional anatomy, the pipeline automatically identifies safe screw corridors and generates individualized insertion trajectories to support clinical preoperative planning. We evaluated the proposed pipeline on 200 clinical cases of pelvic fractures. Compared with conventional manual measurements, the safety margin of the safe insertion corridors increased by 2% across the four screw types, the mean planning time…
PDF arXiv
arXiv cs.CVSep 29
You Cannot Recover What Was Never Measured: Quantifying the Information Ceiling of Ultra-Low-Field MRI Super-Resolution
Prathamesh Pradeep Khole, Shreya Handa, Utkarsh Gupta, Razvan Marinescu
Generative super-resolution models can turn portable 64 mT MRI into images that look like 3T scans, and the field evaluates them with PSNR, SSIM, and pixelwise uncertainty, most often on pairs built by synthetically degrading high-field images. Prior work acknowledges that these models hallucinate and that the problem is ill posed, but to our knowledge no study measures how much information about the individual subject the real low-field scan actually contains. We measure it. Using paired 64 mT and 3T scans of the same subjects from three public datasets, and a measurement protocol validated on tests whose correct answer is known in advance, we find that, judged over the whole brain, real 64 mT scans carry structure specific to the individual only down to approximately 3 to 4 mm half-pitch in plane, and coarser still through plane. Standard synthetic degradations preserve subject information roughly 1 mm beyond this ceiling, so models trained and benchmarked on synthetic pairs are evaluated on information that real scanners never record. We then test trained diffusion models and a publicly released external model on real paired acquisitions; 24 trained runs of five architectures…
PDF arXiv

Activity · 12 weeks

Open problems in Medical image segmentation

Lack of benchmarks aligning with real clinical use2 papers raise it · 2 address it
Current evaluations do not reflect how clinicians actually use AI, risking misjudgment of model readiness and safety.
- Clinician use of language models diverges from how the models are evaluatedraises · addresses
- MoveBench: A Rubric-Based Benchmark for Evaluating Large Language Models in Sports and Health Coachingraises · addresses
Handling incidental or irrelevant information in clinical data1 paper raises it · 0 address it
Models may incorporate or misattribute non-clinical content, leading to unsafe or inaccurate outputs.
- Large language models are vulnerable to incidental information in clinical documentation and reasoningraises
Temporal modeling of multimodal clinical trajectories1 paper raises it · 1 addresses it
Accurate risk prediction requires integrating irregular, heterogeneous data over time, which current methods underaddress.
- TRACE: Tracking Multimodal Clinical Trajectories for Inpatient Risk Predictionraises · addresses
Privacy-preserving learning from deployment experience1 paper raises it · 1 addresses it
Frozen models cannot adapt to new clinical evidence without retraining, which is often impractical or unsafe.
- Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AIraises · addresses
Gigapixel whole-slide image generation and segmentation1 paper raises it · 1 addresses it
Existing generative models operate at patch scale, limiting their utility for slide-level tasks like diagnosis.
- BigCell: Generating Gigapixel Whole-Slide Imagesraises · addresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 57m ago

Medical image segmentation

Found by the topic model: medical image segmentation, clinical reasoning, pathology foundation models.

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