An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation

• URL: http://arxiv.org/abs/2208.07417v1
• Date: Mon, 15 Aug 2022 19:40:18 GMT
• Title: An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation
• Authors: Abhishek Srivastava, Debesh Jha, Elif Keles, Bulent Aydogan, Mohamed Abazeed, Ulas Bagci
• Abstract summary: We propose a new OAR segmentation framework called OARFocalFuseNet. It fuses multi-scale features and employs focal modulation for capturing global-local context across multiple scales. Our best performing method (OARFocalFuseNet) obtained a dice coefficient of 0.7995 and hausdorff distance of 5.1435 on OpenKBP datasets.
• Score: 2.6770199357488242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonstrated a tremendous capacity for 2D medical image segmentation. The key to their success is aggregating global context and maintaining high resolution representations. However, when translated into 3D segmentation problems, existing multi-scale fusion architectures might underperform due to their heavy computation overhead and substantial data diet. To address this issue, we propose a new OAR segmentation framework, called OARFocalFuseNet, which fuses multi-scale features and employs focal modulation for capturing global-local context across multiple scales. Each resolution stream is enriched with features from different resolution scales, and multi-scale information is aggregated to model diverse contextual ranges. As a result, feature representations are further boosted. The comprehensive comparisons in our experimental setup with OAR segmentation as well as multi-organ segmentation show that our proposed OARFocalFuseNet outperforms the recent state-of-the-art methods on publicly available OpenKBP datasets and Synapse multi-organ segmentation. Both of the proposed methods (3D-MSF and OARFocalFuseNet) showed promising performance in terms of standard evaluation metrics. Our best performing method (OARFocalFuseNet) obtained a dice coefficient of 0.7995 and hausdorff distance of 5.1435 on OpenKBP datasets and dice coefficient of 0.8137 on Synapse multi-organ segmentation dataset.

Related papers

• Enhanced Semantic Segmentation for Large-Scale and Imbalanced Point Clouds [6.253217784798542]
  Small-sized objects are prone to be under-sampled or misclassified due to their low occurrence frequency. We propose the Multilateral Cascading Network (MCNet) for large-scale and sample-imbalanced point cloud scenes.
  arXiv  Detail & Related papers  (2024-09-21T02:23:01Z)
• LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing [25.016421338677816]
  Current methods often process only two types of data, missing out on the rich information that additional modalities can provide. We propose a novel textbfLightweight textbfMultimodal data textbfFusion textbfNetwork (LMFNet) LMFNet accommodates various data types simultaneously, including RGB, NirRG, and DSM, through a weight-sharing, multi-branch vision transformer.
  arXiv  Detail & Related papers  (2024-04-21T13:29:42Z)
• Open-vocabulary Panoptic Segmentation with Embedding Modulation [71.15502078615587]
  Open-vocabulary image segmentation is attracting increasing attention due to its critical applications in the real world. Traditional closed-vocabulary segmentation methods are not able to characterize novel objects, whereas several recent open-vocabulary attempts obtain unsatisfactory results. We propose OPSNet, an omnipotent and data-efficient framework for Open-vocabulary Panopticon.
  arXiv  Detail & Related papers  (2023-03-20T17:58:48Z)
• AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation [86.44683367028914]
  Aerial imagery segmentation has some unique challenges, the most critical one among which lies in foreground-background imbalance. We propose Adaptive Focus Framework (AF$), which adopts a hierarchical segmentation procedure and focuses on adaptively utilizing multi-scale representations. AF$ has significantly improved the accuracy on three widely used aerial benchmarks, as fast as the mainstream method.
  arXiv  Detail & Related papers  (2022-02-18T10:14:45Z)
• Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images [1.898617934078969]
  In this paper, a novel segmentation scheme is proposed for the infections of COVID-19 on CT images. A deep collaborative supervision scheme is proposed to guide the network learning the features of edges and semantics. The effectiveness of the proposed scheme is demonstrated on four various COVID-19 CT datasets.
  arXiv  Detail & Related papers  (2021-12-20T07:32:39Z)
• GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation [12.086664133486144]
  Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy.
  arXiv  Detail & Related papers  (2021-11-20T15:41:59Z)
• Manifold Topology Divergence: a Framework for Comparing Data Manifolds [109.0784952256104]
  We develop a framework for comparing data manifold, aimed at the evaluation of deep generative models. Based on the Cross-Barcode, we introduce the Manifold Topology Divergence score (MTop-Divergence) We demonstrate that the MTop-Divergence accurately detects various degrees of mode-dropping, intra-mode collapse, mode invention, and image disturbance.
  arXiv  Detail & Related papers  (2021-06-08T00:30:43Z)
• Deep ensembles based on Stochastic Activation Selection for Polyp Segmentation [82.61182037130406]
  This work deals with medical image segmentation and in particular with accurate polyp detection and segmentation during colonoscopy examinations. Basic architecture in image segmentation consists of an encoder and a decoder. We compare some variant of the DeepLab architecture obtained by varying the decoder backbone.
  arXiv  Detail & Related papers  (2021-04-02T02:07:37Z)
• A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection [74.88284082187462]
  One common strategy is to adopt dilated convolutions in the backbone networks to extract high-resolution feature maps. We propose one novel holistically-guided decoder which is introduced to obtain the high-resolution semantic-rich feature maps.
  arXiv  Detail & Related papers  (2020-12-18T10:51:49Z)
• Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU [87.48110331544885]
  We propose a novel training methodology to train and scale the existing semantic segmentation models. We demonstrate a clear benefit of our approach on a dataset with 1284 classes, bootstrapped from LVIS and COCO annotations, with three times better mIoU than the DeeplabV3+ model.
  arXiv  Detail & Related papers  (2020-12-14T13:12:38Z)
• UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation [20.558512044987125]
  We propose a novel UNet 3+, which takes advantage of full-scale skip connections and deep supervisions. The proposed method is especially benefiting for organs that appear at varying scales.
  arXiv  Detail & Related papers  (2020-04-19T08:05:59Z)

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.