Research

Jump to: Other

11 pages, 2217 KiB

Open AccessArticle

Encoder-Weighted W-Net for Unsupervised Segmentation of Cervix Region in Colposcopy Images

by Jinhee Park, Hyunmo Yang, Hyun-Jin Roh, Woonggyu Jung and Gil-Jin Jang

Cancers 2022, 14(14), 3400; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14143400 - 13 Jul 2022

Cited by 2 | Viewed by 1606

Cervical cancer can be prevented and treated better if it is diagnosed early. Colposcopy, a way of clinically looking at the cervix region, is an efficient method for cervical cancer screening and its early detection. The cervix region segmentation significantly affects the performance [...] Read more.

Cervical cancer can be prevented and treated better if it is diagnosed early. Colposcopy, a way of clinically looking at the cervix region, is an efficient method for cervical cancer screening and its early detection. The cervix region segmentation significantly affects the performance of computer-aided diagnostics using a colposcopy, particularly cervical intraepithelial neoplasia (CIN) classification. However, there are few studies of cervix segmentation in colposcopy, and no studies of fully unsupervised cervix region detection without image pre- and post-processing. In this study, we propose a deep learning-based unsupervised method to identify cervix regions without pre- and post-processing. A new loss function and a novel scheduling scheme for the baseline W-Net are proposed for fully unsupervised cervix region segmentation in colposcopy. The experimental results showed that the proposed method achieved the best performance in the cervix segmentation with a Dice coefficient of 0.71 with less computational cost. The proposed method produced cervix segmentation masks with more reduction in outliers and can be applied before CIN detection or other diagnoses to improve diagnostic performance. Our results demonstrate that the proposed method not only assists medical specialists in diagnosis in practical situations but also shows the potential of an unsupervised segmentation approach in colposcopy. Full article

(This article belongs to the Special Issue Deep Neural Networks for Cancer Screening and Classification)

► Show Figures

Figure 1

14 pages, 4477 KiB

Open AccessArticle

Convolutional Neural Networks to Detect Vestibular Schwannomas on Single MRI Slices: A Feasibility Study

by Carole Koechli, Erwin Vu, Philipp Sager, Lukas Näf, Tim Fischer, Paul M. Putora, Felix Ehret, Christoph Fürweger, Christina Schröder, Robert Förster, Daniel R. Zwahlen, Alexander Muacevic and Paul Windisch

Cancers 2022, 14(9), 2069; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14092069 - 20 Apr 2022

Viewed by 2129

Abstract

In this study. we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. A pretrained CNN (ResNet-34) was retrained and internally validated using contrast-enhanced T1-weighted (T1c) MRI slices from one institution. In a second step, [...] Read more.

In this study. we aimed to detect vestibular schwannomas (VSs) in individual magnetic resonance imaging (MRI) slices by using a 2D-CNN. A pretrained CNN (ResNet-34) was retrained and internally validated using contrast-enhanced T1-weighted (T1c) MRI slices from one institution. In a second step, the model was externally validated using T1c- and T1-weighted (T1) slices from a different institution. As a substitute, bisected slices were used with and without tumors originating from whole transversal slices that contained part of the unilateral VS. The model predictions were assessed based on the categorical accuracy and confusion matrices. A total of 539, 94, and 74 patients were included for training, internal validation, and external T1c validation, respectively. This resulted in an accuracy of 0.949 (95% CI 0.935–0.963) for the internal validation and 0.912 (95% CI 0.866–0.958) for the external T1c validation. We suggest that 2D-CNNs might be a promising alternative to 2.5-/3D-CNNs for certain tasks thanks to the decreased demand for computational power and the fact that there is no need for segmentations. However, further research is needed on the difference between 2D-CNNs and more complex architectures. Full article

(This article belongs to the Special Issue Deep Neural Networks for Cancer Screening and Classification)

► Show Figures

Figure 1

Other

Jump to: Research

15 pages, 704 KiB

Open AccessSystematic Review

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

by Paul Windisch, Carole Koechli, Susanne Rogers, Christina Schröder, Robert Förster, Daniel R. Zwahlen and Stephan Bodis

Cancers 2022, 14(11), 2676; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers14112676 - 27 May 2022

Cited by 6 | Viewed by 2496

Abstract

Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE [...] Read more.

Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusions: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice. Full article

(This article belongs to the Special Issue Deep Neural Networks for Cancer Screening and Classification)

► Show Figures