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The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild. Cancers 2023, 15, 4861 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cancers15194861  \nAcademic Editor: Gerardo Ferrara  \nReceived: 18 August 2023  \nRevised: 1 October 2023  \nAccepted: 2 October 2023  \nPublished: 5 October 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science & Engineering, School of Engineering, University of Ioannina,  \n45110 Ioannina, Greece; [panderekas@gmail.com](panderekas@gmail.com) (P.D.); [arly@cs.uoi.gr](arly@cs.uoi.gr) (A.L.)  \n2 Department of Medical Physics, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece  \n3 Department of Skin and Venereal Diseases, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; [athanasiazampeta@gmail.com](athanasiazampeta@gmail.com) (A.Z.); [ggaitan@uoi.gr](ggaitan@uoi.gr) (G.G.); [ibassuka@uoi.gr](ibassuka@uoi.gr) (I.B.)  \n* [Correspondence: pspyrid@uoi.gr](Correspondence: pspyrid@uoi.gr); Tel.: +30-2651007782  \nSimple Summary: Understanding the relationship between the skin cancerization field and actinic keratosis (AK) is crucial for identifying high-risk individuals, implementing early interventions, and preventing the progression to more aggressive forms of skin cancer. Currently, the clinical tools for grading field cancerization primarily involve assessing AK burden. In addition to their inherent subjectivity, these grading systems are limited by the high degree of AK lesions' recurrence. The present study proposes a method based on deep learning and semantic segmentation to improve the monitoring of the AK burden in clinical settings with enhanced automation and precision. The experimental results highlight the effectiveness of the proposed method, paving the way for more effective and reliable evaluation, continuous monitoring of condition progression, and assessment of treatment responses.  \nAbstract: AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of the AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (i.e., AKU-Net) . AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to the low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512 􀀂 512 pixels were extracted using translation lesion boxes that encompassed lesions in different positions and captured different contexts of perilesional skin. In total, 16,488 translation-augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-Net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improvi","cbCaigxlHWynaLwA","https://ap.wps.com/l/cbCaigxlHWynaLwA","pdf",4819688,17,"English","# 1. Introduction\n# 2. Methods\n## 2.1 Model Architecture (AKU-Net)\n## 2.2 Dataset and Preprocessing\n## 2.3 Training and Testing\n# 3. Results\n# 4. Discussion\n# 5. Conclusions","[{\"question\":\"What problem does the study address in actinic keratosis monitoring?\",\"answer\":\"It targets the need for more automated and precise monitoring of the actinic keratosis (AK) burden in clinical settings, where current approaches can be subjective and limited by lesion recurrence.\"},{\"question\":\"What is AKU-Net and how does it improve segmentation?\",\"answer\":\"AKU-Net is a semantic segmentation model built on U-Net, using transfer learning to work with a small annotated dataset and a convLSTM-based recurrent process to exploit contextual information and manage low-contrast, ambiguous boundaries.\"},{\"question\":\"How was the model trained and evaluated?\",\"answer\":\"The study trained on annotated clinical photographs from 115 patients by extracting 512×512 patches via translation lesion boxes, then tested using lesion center crops. It also compared AKU-Net with plain U-Net and U-Net++ to assess improvements in automation and precision.\"}]","The Promise of Semantic Segmentation in Detecting Actinic Keratosis Using Clinical Photography in the Wild | PDF",1790229428,43]