A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI

Byrne, Nick; Clough, James R.; Montana, Giovanni; King, Andrew P.

doi:10.1007/978-3-030-68107-4_1

Cited by 18 publications

(17 citation statements)

References 32 publications

(51 reference statements)

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“…As such, we can assign each cell complex an ordered numerical value corresponding to the stage of the binarization sequence at which it was first activated (Figure 1e). Mathematically, this is known as a filtration scheme, and each stage of the filtration will correspond to a different simplicial complex (Figure 1f-h) [18, 19]. The topological features of a simplicial complex can be described by the mathematical constructs known as homology classes.…”

Section: Resultsmentioning

confidence: 99%

Topology-based fluorescence image analysis for automated cell identification and segmentation

Panconi

Makarova

Lambert

et al. 2022

Preprint

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Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily-shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally-sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.

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Section: Resultsmentioning

confidence: 99%

Topology-based fluorescence image analysis for automated cell identification and segmentation

Panconi

Makarova

Lambert

et al. 2022

Preprint

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“…The topological features calculated using persistent homology have a big importance in image segmentation [42][43][44]. We compute topological features over windows in glands.…”

Section: Proposed Methodologymentioning

confidence: 99%

Machine learning techniques on homological persistence features for prostate cancer diagnosis

et al. 2022

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The rapid evolution of image processing equipment and techniques ensures the development of novel picture analysis methodologies. One of the most powerful yet computationally possible algebraic techniques for measuring the topological characteristics of functions is persistent homology. It's an algebraic invariant that can capture topological details at different spatial resolutions. Persistent homology investigates the topological features of a space using a set of sampled points, such as pixels. It can track the appearance and disappearance of topological features caused by changes in the nested space created by an operation known as filtration, in which a parameter scale, in our case the intensity of pixels, is increased to detect changes in the studied space over a range of varying scales. In addition, at the level of machine learning there were many studies and articles witnessing recently the combination between homological persistence and machine learning algorithms. On another level, prostate cancer is diagnosed referring to a scoring criterion describing the severity of the cancer called Gleason score. The classical Gleason system defines five histological growth patterns (grades). In our study we propose to study the Gleason score on some glands issued from a new optical microscopy technique called SLIM. This new optical microscopy technique that combines two classic ideas in light imaging: Zernike’s phase contrast microscopy and Gabor’s holography. Persistent homology features are computed on these images. We suggested machine learning methods to classify these images into the corresponding Gleason score. Machine learning techniques applied on homological persistence features was very effective in the detection of the right Gleason score of the prostate cancer in these kinds of images and showed an accuracy of above 95%.

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“…Other types of anatomical priors such as star shape prior [108], [118]- [121], convex shape prior [122], topology [123]- [127], size [128]- [130], etc., have also been introduced to improve the segmentation robustness and anatomically accuracy.…”

Section: Prior Knowledge Learningmentioning

confidence: 99%

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

Wang

2021

IEEE Access

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Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks. The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling. Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful application of deep learning models in medical image segmentation. However, due to its intrinsic difficulty, segmentation with limited supervision is challenging and specific model design and/or learning strategies are needed. In this paper, we provide a systematic and up-to-date review of the solutions above, with summaries and comments about the methodologies. We also highlight several problems in this field, discussed future directions observing further investigations.

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A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI

Cited by 18 publications

References 32 publications

Topology-based fluorescence image analysis for automated cell identification and segmentation

Topology-based fluorescence image analysis for automated cell identification and segmentation

Machine learning techniques on homological persistence features for prostate cancer diagnosis

Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models

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