Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging

• URL: http://arxiv.org/abs/2507.21608v1
• Date: Tue, 29 Jul 2025 09:05:01 GMT
• Title: Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging
• Authors: Maoquan Zhang, Bisser Raytchev, Xiujuan Sun,
• Abstract summary: We show that a carefully configured DeepLabv3 model can achieve high performance in segmenting induced pluripotent stem (iPS) cell colonies.<n>We also offer an open-source implementation that includes strategies for small datasets and domain-specific encoding.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical image segmentation requires not only accuracy but also robustness under challenging imaging conditions. In this study, we show that a carefully configured DeepLabv3 model can achieve high performance in segmenting induced pluripotent stem (iPS) cell colonies, and, under our experimental conditions, outperforms large-scale foundation models such as SAM2 and its medical variant MedSAM2 without structural modifications. These results suggest that, for specialized tasks characterized by subtle, low-contrast boundaries, increased model complexity does not necessarily translate to better performance. Our work revisits the assumption that ever-larger and more generalized architectures are always preferable, and provides evidence that appropriately adapted, simpler models may offer strong accuracy and practical reliability in domain-specific biomedical applications. We also offer an open-source implementation that includes strategies for small datasets and domain-specific encoding, with the aim of supporting further advances in semantic segmentation for regenerative medicine and related fields.

Related papers

• Overfitting in Histopathology Model Training: The Need for Customized Architectures [1.024113475677323]
  We show that fine-tuning large-scale models for image analysis leads to suboptimal performance and significant overfitting when applied to histopathology tasks.<n>Our findings emphasize the need for customized architectures specifically designed for histopathology image analysis.
  arXiv  Detail & Related papers  (2025-06-19T22:05:54Z)
• Dynamically evolving segment anything model with continuous learning for medical image segmentation [50.92344083895528]
  We introduce EvoSAM, a dynamically evolving medical image segmentation model.<n>EvoSAM continuously accumulates new knowledge from an ever-expanding array of scenarios and tasks.<n>Experiments conducted by surgical clinicians on blood vessel segmentation confirm that EvoSAM enhances segmentation efficiency based on user prompts.
  arXiv  Detail & Related papers  (2025-03-08T14:37:52Z)
• Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation [30.524999223901645]
  We propose an enhanced Segment Anything Model (SAM) framework that utilizes annotation-efficient prompts generated in a fully unsupervised fashion.<n>We adopt the direct preference optimization technique to design an optimal policy that enables the model to generate high-fidelity segmentations.<n>State-of-the-art performance of our framework in tasks such as lung segmentation, breast tumor segmentation, and organ segmentation across various modalities, including X-ray, ultrasound, and abdominal CT, justifies its effectiveness in low-annotation data scenarios.
  arXiv  Detail & Related papers  (2025-03-06T17:28:48Z)
• Efficient MedSAMs: Segment Anything in Medical Images on Laptop [69.28565867103542]
  We organized the first international competition dedicated to promptable medical image segmentation.<n>The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline.<n>The best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption.
  arXiv  Detail & Related papers  (2024-12-20T17:33:35Z)
• Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive Manufacturing [1.8547557605937304]
  Segment Anything Model (SAM) is designed for general-purpose image segmentation.<n>In this work, we explore the application and limitations of SAM for industrial X-ray CT inspection of additive manufacturing components.<n>We propose a fine-tuning strategy utilizing parameter-efficient techniques, specifically Conv-LoRa, to adapt SAM for material-specific datasets.
  arXiv  Detail & Related papers  (2024-12-16T02:11:19Z)
• LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models [59.961172635689664]
  "Knowledge Decomposition" aims to improve the performance on specific medical tasks. We propose a novel framework named Low-Rank Knowledge Decomposition (LoRKD) LoRKD explicitly separates gradients from different tasks by incorporating low-rank expert modules and efficient knowledge separation convolution.
  arXiv  Detail & Related papers  (2024-09-29T03:56:21Z)
• MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation [2.2585213273821716]
  We introduce MedCLIP-SAMv2, a novel framework that integrates the CLIP and SAM models to perform segmentation on clinical scans.<n>Our approach includes fine-tuning the BiomedCLIP model with a new Decoupled Hard Negative Noise Contrastive Estimation (DHN-NCE) loss.<n>We also investigate using zero-shot segmentation labels within a weakly supervised paradigm to enhance segmentation quality further.
  arXiv  Detail & Related papers  (2024-09-28T23:10:37Z)
• PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images [11.373941923130305]
  PAM (Propagating Anything Model) is a segmentation approach that uses a 2D prompt, like a bounding box or sketch, to create a complete 3D segmentation of medical image volumes. It significantly outperformed existing models like MedSAM and SegVol, with an average improvement of over 18.1% in dice similarity coefficient (DSC) across 44 medical datasets and various object types.
  arXiv  Detail & Related papers  (2024-08-25T13:42:47Z)
• SAM-Med3D-MoE: Towards a Non-Forgetting Segment Anything Model via Mixture of Experts for 3D Medical Image Segmentation [36.95030121663565]
  Supervised Finetuning (SFT) serves as an effective way to adapt foundation models for task-specific downstream tasks. We propose SAM-Med3D-MoE, a novel framework that seamlessly integrates task-specific finetuned models with the foundational model. Our experiments demonstrate the efficacy of SAM-Med3D-MoE, with an average Dice performance increase from 53 to 56.4 on 15 specific classes.
  arXiv  Detail & Related papers  (2024-07-06T03:03:45Z)
• Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation [113.5002649181103]
  Training open-source small multimodal models (SMMs) to bridge competency gaps for unmet clinical needs in radiology. For training, we assemble a large dataset of over 697 thousand radiology image-text pairs. For evaluation, we propose CheXprompt, a GPT-4-based metric for factuality evaluation, and demonstrate its parity with expert evaluation. The inference of LlaVA-Rad is fast and can be performed on a single V100 GPU in private settings, offering a promising state-of-the-art tool for real-world clinical applications.
  arXiv  Detail & Related papers  (2024-03-12T18:12:02Z)
• PathLDM: Text conditioned Latent Diffusion Model for Histopathology [62.970593674481414]
  We introduce PathLDM, the first text-conditioned Latent Diffusion Model tailored for generating high-quality histopathology images. Our approach fuses image and textual data to enhance the generation process. We achieved a SoTA FID score of 7.64 for text-to-image generation on the TCGA-BRCA dataset, significantly outperforming the closest text-conditioned competitor with FID 30.1.
  arXiv  Detail & Related papers  (2023-09-01T22:08:32Z)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)

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.