Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains

• URL: http://arxiv.org/abs/2512.13534v1
• Date: Mon, 15 Dec 2025 17:00:21 GMT
• Title: Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains
• Abstract summary: We introduce Pancakes, a framework that automatically generates multi-label segmentation maps for multiple plausible protocols.<n>We demonstrate that our model can significantly outperform existing foundation models in producing several plausible whole-image segmentations.
• Score: 16.024959376939158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A single biomedical image can be meaningfully segmented in multiple ways, depending on the desired application. For instance, a brain MRI can be segmented according to tissue types, vascular territories, broad anatomical regions, fine-grained anatomy, or pathology, etc. Existing automatic segmentation models typically either (1) support only a single protocol, the one they were trained on, or (2) require labor-intensive manual prompting to specify the desired segmentation. We introduce Pancakes, a framework that, given a new image from a previously unseen domain, automatically generates multi-label segmentation maps for multiple plausible protocols, while maintaining semantic consistency across related images. Pancakes introduces a new problem formulation that is not currently attainable by existing foundation models. In a series of experiments on seven held-out datasets, we demonstrate that our model can significantly outperform existing foundation models in producing several plausible whole-image segmentations, that are semantically coherent across images.

Related papers

• ProGiDiff: Prompt-Guided Diffusion-Based Medical Image Segmentation [12.964514627034122]
  We propose a novel framework called ProGiDiff that leverages existing image generation models for medical image segmentation purposes.<n> Specifically, we propose a ControlNet-style conditioning mechanism with a custom encoder, suitable for image conditioning, to steer a pre-trained diffusion model to output segmentation masks.<n>Our experiment on organ segmentation from CT images demonstrates strong performance compared to previous methods and could greatly benefit from an expert-in-the-loop setting.
  arXiv  Detail & Related papers  (2026-01-22T15:56:21Z)
• Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation [49.5901368256326]
  We propose a novel Domain-Adaptive Prompt framework for fine-tuning the Segment Anything Model (termed as DAPSAM) in segmenting medical images. Our DAPSAM achieves state-of-the-art performance on two medical image segmentation tasks with different modalities.
  arXiv  Detail & Related papers  (2024-09-19T07:28:33Z)
• Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images [66.79688768141814]
  We develop an automatic cell classification pipeline to label microscopy images. We then train a classification model based on the category labels. We deploy two types of segmentation models to segment cells with roundish and irregular shapes.
  arXiv  Detail & Related papers  (2023-10-22T08:11:08Z)
• SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation [27.044797468878837]
  The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images. We propose a novel method that combines the SAM with domain-specific knowledge for reliable utilization of unlabeled images. Our work initiates a new direction of semi-supervised learning for medical image segmentation.
  arXiv  Detail & Related papers  (2023-08-26T04:46:10Z)
• One-Prompt to Segment All Medical Images [18.829371793411347]
  This paper introduces a new paradigm toward the universal medical image segmentation, termed 'One-Prompt' One-Prompt combines the strengths of one-shot and interactive methods. In the inference stage, with just textbfone prompted sample, it can adeptly handle the unseen task in a single forward pass. Tested on 14 previously unseen datasets, the One-Prompt Model showcases superior zero-shot segmentation capabilities, outperforming a wide range of related methods.
  arXiv  Detail & Related papers  (2023-05-17T15:37:47Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Multi-center anatomical segmentation with heterogeneous labels via landmark-based models [5.1618890441642]
  We show how state-of-the-art pixel-level segmentation models fail in naively learning this task due to domain issues and conflicting labels. We then propose to adopt HybridGNet, a landmark-based segmentation model which learns the available anatomical structures using graph-based representations.
  arXiv  Detail & Related papers  (2022-11-14T14:25:52Z)
• A Generalist Framework for Panoptic Segmentation of Images and Videos [61.61453194912186]
  We formulate panoptic segmentation as a discrete data generation problem, without relying on inductive bias of the task. A diffusion model is proposed to model panoptic masks, with a simple architecture and generic loss function. Our method is capable of modeling video (in a streaming setting) and thereby learns to track object instances automatically.
  arXiv  Detail & Related papers  (2022-10-12T16:18:25Z)
• Using Soft Labels to Model Uncertainty in Medical Image Segmentation [0.0]
  We propose a simple method to obtain soft labels from the annotations of multiple physicians. For each image, our method produces a single well-calibrated output that can be thresholded at multiple confidence levels. We evaluated our method on the MICCAI 2021 QUBIQ challenge, showing that it performs well across multiple medical image segmentation tasks.
  arXiv  Detail & Related papers  (2021-09-26T14:47:18Z)
• Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation [75.58395328700821]
  We propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing.
  arXiv  Detail & Related papers  (2021-07-07T19:06:34Z)
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization [112.68171734288237]
  We propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. We learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of unlabeled images. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization.
  arXiv  Detail & Related papers  (2021-04-12T21:41:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.