KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representations

Jose, Jeya Maria; Sindagi, Vishwanath A.; Hacihaliloglu, Ilker; Patel, Vishal M.

doi:10.48550/arxiv.2006.04878

Cited by 2 publications

(1 citation statement)

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“…The authors used this method to segment the fetal head in ultrasound images and the optic disc and optic cup in color fundus images. Valanarasu et al [ 27 ] presented a network architecture called KiU-Net, which projects data onto higher dimensions and picks finer details when compared to a standard U-Net. The suggested method addressed the performance failures when segmenting smaller anatomical structures with blurred, noisy boundaries.…”

Section: Introductionmentioning

confidence: 99%

HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy

et al. 2022

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Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles’ diameter is usually in the range of 2–10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the number of antral follicles. Manual follicle measurement is inhibited by operator time, expertise and the subjectivity of delineating the two axes of the follicles. This necessitates an automated framework capable of quantifying follicle size and count in a clinical setting. This paper proposes a novel Harmonic Attention-based U-Net network, HaTU-Net, to precisely segment the ovary and follicles in ultrasound images. We replace the standard convolution operation with a harmonic block that convolves the features with a window-based discrete cosine transform (DCT). Additionally, we proposed a harmonic attention mechanism that helps to promote the extraction of rich features. The suggested technique allows for capturing the most relevant features, such as boundaries, shape, and textural patterns, in the presence of various noise sources (i.e., shadows, poor contrast between tissues, and speckle noise). We evaluated the proposed model on our in-house private dataset of 197 patients undergoing TransVaginal UltraSound (TVUS) exam. The experimental results on an independent test set confirm that HaTU-Net achieved a Dice coefficient score of 90% for ovaries and 81% for antral follicles, an improvement of 2% and 10%, respectively, when compared to a standard U-Net. Further, we accurately measure the follicle size, yielding the recall, and precision rates of 91.01% and 76.49%, respectively.

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Section: Introductionmentioning

confidence: 99%