Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View

• URL: http://arxiv.org/abs/2206.14746v1
• Date: Wed, 29 Jun 2022 16:18:55 GMT
• Title: Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View
• Authors: Veronika A. Zimmer, Alberto Gomez, Emily Skelton, Robert Wright, Gavin Wheeler, Shujie Deng, Nooshin Ghavami, Karen Lloyd, Jacqueline Matthew, Bernhard Kainz, Daniel Rueckert, Joseph V. Hajnal, Julia A. Schnabel
• Abstract summary: We propose a multi-task learning approach that combines the classification of placental location and semantic placenta segmentation in a single convolutional neural network. Our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation.
• Score: 12.271784950642344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation. In this work, we address these three challenges with a multi-task learning approach that combines the classification of placental location (e.g., anterior, posterior) and semantic placenta segmentation in a single convolutional neural network. Through the classification task the model can learn from larger and more diverse datasets while improving the accuracy of the segmentation task in particular in limited training set conditions. With this approach we investigate the variability in annotations from multiple raters and show that our automatic segmentations (Dice of 0.86 for anterior and 0.83 for posterior placentas) achieve human-level performance as compared to intra- and inter-observer variability. Lastly, our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation. This results in high quality segmentation of larger structures such as the placenta in US with reduced image artifacts which are beyond the field-of-view of single probes.

Related papers

• Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans [12.090385175034305]
  Liver lesions vary significantly in their size, shape, texture, and contrast with respect to surrounding tissue. Current state-of-the-art lesion segmentation networks use the encoder-decoder design paradigm based on the UNet architecture. Our approach improves relative liver lesion segmentation performance by 1.6% while reducing performance variability across subjects by 8% when compared to the current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-04-17T08:05:04Z)
• WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep Imaging Ultrasound [1.2903292694072621]
  Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture quality ultrasound images. We present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet) In this model, we incorporate wavelet gates (WGs) and attention gates (AGs) between the encoder and decoder instead of a simple connection to overcome the limitations mentioned.
  arXiv  Detail & Related papers  (2023-11-17T20:32:37Z)
• Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model [0.0]
  We propose a framework that uses images generated by a Latent Diffusion Model (LDM) as unlabeled images for semi-supervised learning. Our approach enables the effective transfer of probability distribution knowledge to the segmentation network, resulting in improved segmentation accuracy.
  arXiv  Detail & Related papers  (2023-05-16T14:08:24Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference [28.409679398886304]
  We develop a novel model named Mixed-UNet, which has two parallel branches in the decoding phase. We evaluate the designed Mixed-UNet against several prevalent deep learning-based segmentation approaches on our dataset collected from the local hospital and public datasets.
  arXiv  Detail & Related papers  (2022-05-06T08:37:02Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset [57.30136148318641]
  Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS) This may lead to increased procedural time and incomplete ablation, resulting in persistent TTTS. Computer-assisted intervention may help overcome these challenges by expanding the fetoscopic field of view through video mosaicking and providing better visualization of the vessel network. We present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
  arXiv  Detail & Related papers  (2021-06-10T17:14:27Z)
• Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet [74.22397862400177]
  We propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation. With the detected catheter, patch-based Dual-UNet is applied to segment the catheter in 3D volumetric data.
  arXiv  Detail & Related papers  (2020-06-25T21:10:04Z)
• 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)
• Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound [59.105304755899034]
  This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in transrectal ultrasound (TRUS) images. Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance.
  arXiv  Detail & Related papers  (2019-07-03T05:21:52Z)

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.