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Related Concept Videos

Upsampling01:22

Upsampling

740
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Aliasing01:18

Aliasing

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
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Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Sampling Theorem01:15

Sampling Theorem

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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
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Atlas-based under-segmentation.

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    Atlas-based segmentation often under-segments organs. This study introduces a generative model to correct background bias, improving segmentation accuracy by learning and separating background structures.

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    Area of Science:

    • Medical image analysis
    • Computational anatomy

    Background:

    • Atlas-based segmentation is widely used but prone to under-segmentation.
    • Standard error metrics do not differentiate under- from over-segmentation, masking this issue.
    • A common practice of merging surrounding tissues into a single background label introduces bias.

    Purpose of the Study:

    • To address the under-segmentation bias in atlas-based segmentation.
    • To propose a novel generative model for improved organ segmentation.
    • To enhance the accuracy of medical image segmentation.

    Main Methods:

    • Quantified under- and over-segmentation in typical examples.
    • Developed a generative model to learn background structures from data.
    • Implemented an inference method to separate background into distinct structures.

    Main Results:

    • Demonstrated that segmenting only the organ of interest biases atlas estimates towards the background.
    • The proposed generative model effectively corrects this bias.
    • Significant improvements in segmentation accuracy were observed across several applications.

    Conclusions:

    • The proposed generative model successfully mitigates under-segmentation bias in atlas-based segmentation.
    • Learning background structures from data is crucial for accurate segmentation.
    • This approach offers a promising solution for enhancing medical image segmentation accuracy.