Holistic Modeling in Medical Image Segmentation Using Spatial RecurrenceDownload PDF

10 Dec 2021, 14:06 (edited 22 Jun 2022)MIDL 2022Readers: Everyone
  • Keywords: Medical Image Segmentation, U-Net, Spatial Recurrency
  • TL;DR: Introducing spatial recurrency into convolutional neural networks increases accuracy in medical image segmentation.
  • Abstract: In clinical practice, regions of interest in medical imaging (MI) often need to be identified through a process of precise image segmentation. For MI segmentation to generalize, we need two components: to identify local descriptions but at the same time to develop a holistic representation of the image that captures long-range spatial dependencies. Unfortunately, we demonstrate that the start of the art does not achieve the latter. In particular, it does not provide a modeling that yields a global, contextual model. To improve accuracy, and enable holistic modeling, we introduce a novel deep neural network architecture endowed with spatial recurrence. The implementation relies on gated recurrent units that directionally traverse the feature map, greatly increasing each layers receptive field and explicitly modeling non-adjacent relationships between pixels. Our method is evaluated in four different segmentation tasks: nuclei segmentation in microscopy images, colorectal polyp segmentation in colonoscopy videos, liver segmentation in abdominal CT scans, and aorta artery segmentation in thoracic CT scans. Our experiments demonstrate an average increase in performance of 4.72 Dice points and 0.68 Hausdorff distance units compared to U-Net and U-Net++, and a performance better or on par when compared to transformer-based architectures. Code available at https://github.com/JoaoCarv/holistic-seg.
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  • Paper Type: methodological development
  • Primary Subject Area: Segmentation
  • Secondary Subject Area: Application: Radiology
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  • Code And Data: Code: https://github.com/JoaoCarv/holistic-seg Data: DSB2018: https://www.kaggle.com/c/data-science-bowl-2018/data CVC-ClinicDB: https://polyp.grand-challenge.org/CVCClinicDB/ LiTS: https://competitions.codalab.org/competitions/17094 SegTHOR: https://competitions.codalab.org/competitions/21145#learn_the_details-dataset
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