Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images

• URL: http://arxiv.org/abs/2405.17520v4
• Date: Sat, 21 Sep 2024 03:05:31 GMT
• Title: Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images
• Authors: Syed Javed, Tariq M. Khan, Abdul Qayyum, Hamid Alinejad-Rokny, Arcot Sowmya, Imran Razzak,
• Abstract summary: Mini-Net is a lightweight segmentation network specifically designed for medical images. With fewer than 38,000 parameters, Mini-Net efficiently captures both high- and low-frequency features. We evaluate Mini-Net on various datasets, including DRIVE, STARE, ISIC-2016, ISIC-2018, and MoNuSeg.
• Score: 18.48562660373185
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Accurate segmentation of anatomical structures and abnormalities in medical images is crucial for computer-aided diagnosis and analysis. While deep learning techniques excel at this task, their computational demands pose challenges. Additionally, some cutting-edge segmentation methods, though effective for general object segmentation, may not be optimised for medical images. To address these issues, we propose Mini-Net, a lightweight segmentation network specifically designed for medical images. With fewer than 38,000 parameters, Mini-Net efficiently captures both high- and low-frequency features, enabling real-time applications in various medical imaging scenarios. We evaluate Mini-Net on various datasets, including DRIVE, STARE, ISIC-2016, ISIC-2018, and MoNuSeg, demonstrating its robustness and good performance compared to state-of-the-art methods.

Related papers

• Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models [17.461510586128874]
  We propose a novel method that adapts DINOv2 and Segment Anything Model 2 for retrieval-augmented few-shot medical image segmentation. Our approach uses DINOv2's feature as query to retrieve similar samples from limited annotated data, which are then encoded as memories and stored in memory bank.
  arXiv  Detail & Related papers  (2024-08-16T15:48:07Z)
• A hybrid approach for improving U-Net variants in medical image segmentation [0.0]
  The technique of splitting a medical image into various segments or regions of interest is known as medical image segmentation. The segmented images that are produced can be used for many different things, including diagnosis, surgery planning, and therapy evaluation. This research aims to reduce the network parameter requirements using depthwise separable convolutions.
  arXiv  Detail & Related papers  (2023-07-31T07:43:45Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms [9.626726110488386]
  We propose a lightweight dual-multiscale residual block-based computational neural network tailored for medical image segmentation on IoT and edge platforms. LDMRes-Net overcomes limitations with its remarkably low number of learnable parameters (0.072M), making it highly suitable for resource-constrained devices.
  arXiv  Detail & Related papers  (2023-06-09T10:34:18Z)
• MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation [7.587725015524997]
  We propose a multi- kernel image segmentation net (MKIS-Net) MKIS-Net is a light-weight architecture with a small number of trainable parameters. We demonstrate the efficacy of MKIS-Net on several tasks including segmentation of retinal vessels, skin lesion segmentation, and chest X-ray segmentation.
  arXiv  Detail & Related papers  (2022-10-15T02:46:28Z)
• Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask [66.63753229115983]
  We adapt the object proposal generation system AttentionMask and propose a dedicated post-processing to select promising proposals. The results on the ROBUST-MIS Challenge 2019 show that our adapted AttentionMask system is a strong foundation for generating state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-21T21:37:56Z)
• CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects [0.0]
  This paper proposes a Context Axial Reserve Attention Network (CaraNet) to improve the segmentation performance on small objects. Our CaraNet achieves the top-rank mean Dice segmentation accuracy, but also shows a distinct advantage in segmentation of small medical objects.
  arXiv  Detail & Related papers  (2021-08-16T22:48:47Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution [47.56835064059436]
  Multi-dimensional attention segmentation model with cascade multi-scale convolution is proposed to predict accurate segmentation for small objects in medical images. The proposed method is evaluated on three datasets: KiTS19, Pancreas CT of Decathlon-10, and MICCAI 2018 LiTS Challenge.
  arXiv  Detail & Related papers  (2020-11-15T13:54:22Z)
• Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound [74.22397862400177]
  We propose a novel Frustum ultrasound based catheter segmentation method. The proposed method achieved the state-of-the-art performance with an efficiency of 0.25 second per volume.
  arXiv  Detail & Related papers  (2020-10-19T13:56:22Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)

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.