Blurred Lesion Image Segmentation via an Adaptive Scale Thresholding Network Article Swipe
Qi Chen
,
Wenmin Wang
,
Z P Wang
,
Haomei Jia
,
Minglu Zhao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15179259
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15179259
Medical image segmentation is crucial for disease diagnosis, as precise results aid clinicians in locating lesion regions. However, lesions often have blurred boundaries and complex shapes, challenging traditional methods in capturing clear edges and impacting accurate localization and complete excision. Small lesions are also critical but prone to detail loss during downsampling, reducing segmentation accuracy. To address these issues, we propose a novel adaptive scale thresholding network (AdSTNet) that acts as a post-processing lightweight network for enhancing sensitivity to lesion edges and cores through a dual-threshold adaptive mechanism. The dual-threshold adaptive mechanism is a key architectural component that includes a main threshold map for core localization and an edge threshold map for more precise boundary detection. AdSTNet is compatible with any segmentation network and introduces only a small computational and parameter cost. Additionally, Spatial Attention and Channel Attention (SACA), the Laplacian operator, and the Fusion Enhancement module are introduced to improve feature processing. SACA enhances spatial and channel attention for core localization; the Laplacian operator retains edge details without added complexity; and the Fusion Enhancement module adapts concatenation operation and Convolutional Gated Linear Unit (ConvGLU) to improve feature intensities to improve edge and small lesion segmentation. Experiments show that AdSTNet achieves notable performance gains on ISIC 2018, BUSI, and Kvasir-SEG datasets. Compared with the original U-Net, our method attains mIoU/mDice of 83.40%/90.24% on ISIC, 71.66%/80.32% on BUSI, and 73.08%/81.91% on Kvasir-SEG. Moreover, similar improvements are observed in the rest of the networks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15179259
- https://www.mdpi.com/2076-3417/15/17/9259/pdf?version=1755872490
- OA Status
- gold
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413452453
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413452453Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app15179259Digital Object Identifier
- Title
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Blurred Lesion Image Segmentation via an Adaptive Scale Thresholding NetworkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-22Full publication date if available
- Authors
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Qi Chen, Wenmin Wang, Z P Wang, Haomei Jia, Minglu ZhaoList of authors in order
- Landing page
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https://doi.org/10.3390/app15179259Publisher landing page
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https://www.mdpi.com/2076-3417/15/17/9259/pdf?version=1755872490Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/15/17/9259/pdf?version=1755872490Direct OA link when available
- Concepts
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Thresholding, Artificial intelligence, Computer vision, Computer science, Image segmentation, Segmentation, Scale (ratio), Pattern recognition (psychology), Image (mathematics), Geography, CartographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2025-08-22 |
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| referenced_works | https://openalex.org/W2952509531, https://openalex.org/W3158550309, https://openalex.org/W2994888843, https://openalex.org/W3195708760, https://openalex.org/W4200217217, https://openalex.org/W4289844305, https://openalex.org/W4319298404, https://openalex.org/W4367314923, https://openalex.org/W4212875960, https://openalex.org/W4313527399, https://openalex.org/W4387959320, https://openalex.org/W3170019900, https://openalex.org/W4296640326, https://openalex.org/W4391109864, https://openalex.org/W2774320778, https://openalex.org/W3014974815, https://openalex.org/W2953129827, https://openalex.org/W2999580839, https://openalex.org/W2464708700, https://openalex.org/W4313032421, https://openalex.org/W4295934721, https://openalex.org/W3204166336, https://openalex.org/W4319300975, https://openalex.org/W4412425680, https://openalex.org/W4411179808, https://openalex.org/W4322096753, https://openalex.org/W4392769609, https://openalex.org/W4306738766, https://openalex.org/W2980190282, https://openalex.org/W4220710271, https://openalex.org/W4389349065, https://openalex.org/W2074849287, https://openalex.org/W3037844805, https://openalex.org/W1585008090, https://openalex.org/W4250482878, https://openalex.org/W2752782242, https://openalex.org/W4402754177, https://openalex.org/W2194775991, https://openalex.org/W2991372685, https://openalex.org/W4403123719, https://openalex.org/W4403274254, https://openalex.org/W3203480968 |
| referenced_works_count | 42 |
| abstract_inverted_index.a | 61, 71, 84, 93, 99, 126 |
| abstract_inverted_index.To | 55 |
| abstract_inverted_index.an | 107 |
| abstract_inverted_index.as | 8, 70 |
| abstract_inverted_index.in | 13, 29, 236 |
| abstract_inverted_index.is | 3, 92, 117 |
| abstract_inverted_index.of | 220, 239 |
| abstract_inverted_index.on | 204, 222, 225, 229 |
| abstract_inverted_index.to | 47, 78, 149, 185, 189 |
| abstract_inverted_index.we | 59 |
| abstract_inverted_index.The | 88 |
| abstract_inverted_index.aid | 11 |
| abstract_inverted_index.and | 23, 33, 37, 81, 106, 123, 129, 135, 142, 156, 171, 179, 192, 208, 227 |
| abstract_inverted_index.any | 120 |
| abstract_inverted_index.are | 42, 147, 234 |
| abstract_inverted_index.but | 45 |
| abstract_inverted_index.for | 5, 75, 103, 111, 159 |
| abstract_inverted_index.key | 94 |
| abstract_inverted_index.map | 102, 110 |
| abstract_inverted_index.our | 216 |
| abstract_inverted_index.the | 139, 143, 162, 172, 213, 237, 240 |
| abstract_inverted_index.ISIC | 205 |
| abstract_inverted_index.SACA | 153 |
| abstract_inverted_index.Unit | 183 |
| abstract_inverted_index.acts | 69 |
| abstract_inverted_index.also | 43 |
| abstract_inverted_index.core | 104, 160 |
| abstract_inverted_index.edge | 108, 166, 191 |
| abstract_inverted_index.have | 20 |
| abstract_inverted_index.loss | 49 |
| abstract_inverted_index.main | 100 |
| abstract_inverted_index.more | 112 |
| abstract_inverted_index.only | 125 |
| abstract_inverted_index.rest | 238 |
| abstract_inverted_index.show | 197 |
| abstract_inverted_index.that | 68, 97, 198 |
| abstract_inverted_index.with | 119, 212 |
| abstract_inverted_index.2018, | 206 |
| abstract_inverted_index.BUSI, | 207, 226 |
| abstract_inverted_index.Gated | 181 |
| abstract_inverted_index.ISIC, | 223 |
| abstract_inverted_index.Small | 40 |
| abstract_inverted_index.added | 169 |
| abstract_inverted_index.clear | 31 |
| abstract_inverted_index.cores | 82 |
| abstract_inverted_index.cost. | 131 |
| abstract_inverted_index.edges | 32, 80 |
| abstract_inverted_index.gains | 203 |
| abstract_inverted_index.image | 1 |
| abstract_inverted_index.novel | 62 |
| abstract_inverted_index.often | 19 |
| abstract_inverted_index.prone | 46 |
| abstract_inverted_index.scale | 64 |
| abstract_inverted_index.small | 127, 193 |
| abstract_inverted_index.these | 57 |
| abstract_inverted_index.Fusion | 144, 173 |
| abstract_inverted_index.Linear | 182 |
| abstract_inverted_index.U-Net, | 215 |
| abstract_inverted_index.adapts | 176 |
| abstract_inverted_index.detail | 48 |
| abstract_inverted_index.during | 50 |
| abstract_inverted_index.lesion | 15, 79, 194 |
| abstract_inverted_index.method | 217 |
| abstract_inverted_index.module | 146, 175 |
| abstract_inverted_index.(SACA), | 138 |
| abstract_inverted_index.AdSTNet | 116, 199 |
| abstract_inverted_index.Channel | 136 |
| abstract_inverted_index.Medical | 0 |
| abstract_inverted_index.Spatial | 133 |
| abstract_inverted_index.address | 56 |
| abstract_inverted_index.attains | 218 |
| abstract_inverted_index.blurred | 21 |
| abstract_inverted_index.channel | 157 |
| abstract_inverted_index.complex | 24 |
| abstract_inverted_index.crucial | 4 |
| abstract_inverted_index.details | 167 |
| abstract_inverted_index.disease | 6 |
| abstract_inverted_index.feature | 151, 187 |
| abstract_inverted_index.improve | 150, 186, 190 |
| abstract_inverted_index.issues, | 58 |
| abstract_inverted_index.lesions | 18, 41 |
| abstract_inverted_index.methods | 28 |
| abstract_inverted_index.network | 66, 74, 122 |
| abstract_inverted_index.notable | 201 |
| abstract_inverted_index.precise | 9, 113 |
| abstract_inverted_index.propose | 60 |
| abstract_inverted_index.results | 10 |
| abstract_inverted_index.retains | 165 |
| abstract_inverted_index.shapes, | 25 |
| abstract_inverted_index.similar | 232 |
| abstract_inverted_index.spatial | 155 |
| abstract_inverted_index.through | 83 |
| abstract_inverted_index.without | 168 |
| abstract_inverted_index.Compared | 211 |
| abstract_inverted_index.However, | 17 |
| abstract_inverted_index.accurate | 35 |
| abstract_inverted_index.achieves | 200 |
| abstract_inverted_index.adaptive | 63, 86, 90 |
| abstract_inverted_index.boundary | 114 |
| abstract_inverted_index.complete | 38 |
| abstract_inverted_index.critical | 44 |
| abstract_inverted_index.enhances | 154 |
| abstract_inverted_index.includes | 98 |
| abstract_inverted_index.locating | 14 |
| abstract_inverted_index.observed | 235 |
| abstract_inverted_index.operator | 164 |
| abstract_inverted_index.original | 214 |
| abstract_inverted_index.reducing | 52 |
| abstract_inverted_index.regions. | 16 |
| abstract_inverted_index.(AdSTNet) | 67 |
| abstract_inverted_index.(ConvGLU) | 184 |
| abstract_inverted_index.Attention | 134, 137 |
| abstract_inverted_index.Laplacian | 140, 163 |
| abstract_inverted_index.Moreover, | 231 |
| abstract_inverted_index.accuracy. | 54 |
| abstract_inverted_index.attention | 158 |
| abstract_inverted_index.capturing | 30 |
| abstract_inverted_index.component | 96 |
| abstract_inverted_index.datasets. | 210 |
| abstract_inverted_index.enhancing | 76 |
| abstract_inverted_index.excision. | 39 |
| abstract_inverted_index.impacting | 34 |
| abstract_inverted_index.mechanism | 91 |
| abstract_inverted_index.networks. | 241 |
| abstract_inverted_index.operation | 178 |
| abstract_inverted_index.operator, | 141 |
| abstract_inverted_index.parameter | 130 |
| abstract_inverted_index.threshold | 101, 109 |
| abstract_inverted_index.Kvasir-SEG | 209 |
| abstract_inverted_index.boundaries | 22 |
| abstract_inverted_index.clinicians | 12 |
| abstract_inverted_index.compatible | 118 |
| abstract_inverted_index.detection. | 115 |
| abstract_inverted_index.diagnosis, | 7 |
| abstract_inverted_index.introduced | 148 |
| abstract_inverted_index.introduces | 124 |
| abstract_inverted_index.mIoU/mDice | 219 |
| abstract_inverted_index.mechanism. | 87 |
| abstract_inverted_index.Enhancement | 145, 174 |
| abstract_inverted_index.Experiments | 196 |
| abstract_inverted_index.Kvasir-SEG. | 230 |
| abstract_inverted_index.challenging | 26 |
| abstract_inverted_index.complexity; | 170 |
| abstract_inverted_index.intensities | 188 |
| abstract_inverted_index.lightweight | 73 |
| abstract_inverted_index.performance | 202 |
| abstract_inverted_index.processing. | 152 |
| abstract_inverted_index.sensitivity | 77 |
| abstract_inverted_index.traditional | 27 |
| abstract_inverted_index.improvements | 233 |
| abstract_inverted_index.localization | 36, 105 |
| abstract_inverted_index.segmentation | 2, 53, 121 |
| abstract_inverted_index.thresholding | 65 |
| abstract_inverted_index.71.66%/80.32% | 224 |
| abstract_inverted_index.73.08%/81.91% | 228 |
| abstract_inverted_index.83.40%/90.24% | 221 |
| abstract_inverted_index.Additionally, | 132 |
| abstract_inverted_index.Convolutional | 180 |
| abstract_inverted_index.architectural | 95 |
| abstract_inverted_index.computational | 128 |
| abstract_inverted_index.concatenation | 177 |
| abstract_inverted_index.downsampling, | 51 |
| abstract_inverted_index.localization; | 161 |
| abstract_inverted_index.segmentation. | 195 |
| abstract_inverted_index.dual-threshold | 85, 89 |
| abstract_inverted_index.post-processing | 72 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5017052768 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I111950717 |
| citation_normalized_percentile.value | 0.4489194 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |