False Positives Reduction on Segmented Multiple Sclerosis Lesions Using Fuzzy Inference System by Incorporating Atlas Prior Anatomical Knowledge: A Conceptual Model

Khastavaneh, Hassan; Haron, Habibollah

doi:10.1007/978-3-319-11289-3_2

Cited by 3 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several strategies have been proposed to refine lesion segmentation, including the use of lesion location information (Datta & Narayana, 2013), continuity across slices (Abdullah et al, 2011), ratio maps across modalities (Sajja et al, 2006) and classification of FP as outlier clusters far from lesion and not lesion tissues (Lao et al, 2008). Other approaches simultaneously employed information coming from different sources (Abdullah et al, 2011; Battaglini et al, 2014; Ganna et al, 2002; Khastavaneh & Haron, 2014; Roura et al, 2015). A point of strength of our pipeline is that it relies on the objective analysis of the intensity distribution of the pure tissues surrounding the lesions in both 3D and 2D, which solves the problem of local inhomogeneities strongly influencing lesion segmentation.…”

Section: Discussionmentioning

confidence: 99%

BIANCA‐MS: An optimized tool for automated multiple sclerosis lesion segmentation

Gentile,

Jenkinson,

Griffanti

et al. 2023

Human Brain Mapping

View full text Add to dashboard Cite

In this work we present BIANCA‐MS, a novel tool for brain white matter lesion segmentation in multiple sclerosis (MS), able to generalize across both the wide spectrum of MRI acquisition protocols and the heterogeneity of manually labeled data. BIANCA‐MS is based on the original version of BIANCA and implements two innovative elements: a harmonized setting, tested under different MRI protocols, which avoids the need to further tune algorithm parameters to each dataset; and a cleaning step developed to improve consistency in automated and manual segmentations, thus reducing unwanted variability in output segmentations and validation data. BIANCA‐MS was tested on three datasets, acquired with different MRI protocols. First, we compared BIANCA‐MS to other widely used tools. Second, we tested how BIANCA‐MS performs in separate datasets. Finally, we evaluated BIANCA‐MS performance on a pooled dataset where all MRI data were merged. We calculated the overlap using the DICE spatial similarity index (SI) as well as the number of false positive/negative clusters (nFPC/nFNC) in comparison to the manual masks processed with the cleaning step. BIANCA‐MS clearly outperformed other available tools in both high‐ and low‐resolution images and provided comparable performance across different scanning protocols, sets of modalities and image resolutions. BIANCA‐MS performance on the pooled dataset (SI: 0.72 ± 0.25, nFPC: 13 ± 11, nFNC: 4 ± 8) were comparable to those achieved on each individual dataset (median across datasets SI: 0.72 ± 0.28, nFPC: 14 ± 11, nFNC: 4 ± 8). Our findings suggest that BIANCA‐MS is a robust and accurate approach for automated MS lesion segmentation.

show abstract

Section: Discussionmentioning

confidence: 99%

BIANCA‐MS: An optimized tool for automated multiple sclerosis lesion segmentation

Gentile,

Jenkinson,

Griffanti

et al. 2023

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…Khastavaneh et al [214] proposed a method to reduce the number of FPs using a fuzzy inference system. The method consists of a preprocessing step which includes skull removing, brain tissue extraction, registration and normalization of inhomogeneities.…”

Section: Fuzzy Methodsmentioning

confidence: 99%

“…Wu et al [154] 2006 KNN T1-w, T2, PD-w T2-HL Kawa et al [160] 2007 FCM T1, FLAIR MSL Kroon et al [153] 2008 PCA T1, T2, FLAIR MSL Ozyavru et al [157] 2011 FC T1, T2 MSL Bhanumurthy et al [164] 2016 HFFCM T1, T2, FLAIR MSL Xiang et al [217] 2013 FCM T1-w, T2-w, PD-w WML Jain et al [167] 2015 MSmetrix T1-w, FLAIR HL Valverde et al [168] 2014 FCM T1-w Tissue volume Washimkar et al [163] 2015 FCM FLAIR Infected regions Fartari et al [159] 2017 KNN MPRAGE, MP2RAGE; DIR WML, Cortical Lesion Zeng et al [166] 2015 JHM T1, T2, FLAIR MSL Fuzzy Bijar et al [209] 2012 Fuzzy Entropy + GA FLAIR MSL Esposito et al [210] 2010 Fuzzy Rules based WML Aymerich et al [211] 2011 Fuzzy Rules based T2-w MSL Esposito et al [212] 2011 Ontology-based fuzzy decision support (OB-FDS) T1, T2, PD-w, QMCI WML Khastavaneh et al [214] 2014 Fuzzy Inference System FLAIR MSL Table 5: Methods categorization with image sequences rules-based methods [210], [211], fuzzy entropy [214] and other fuzzy-based methods [214], [212], [218].…”

Section: Unsupervisedmentioning

confidence: 99%

A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs

Shah¹,

S.²,

Ahmad³

et al. 2023

Preprint

View full text Add to dashboard Cite

Introduction: Multiple Sclerosis (MS) is a chronic disease that affects millions of people across the globe. MS can critically affect different organs of the central nervous system such as the eyes, the spinal cord, and the brain.Background: To help physicians in diagnosing MS lesions, computer-aided methods are widely used. In this regard, a considerable research has been carried out in the area of automatic detection and segmentation of MS lesions in magnetic resonance images (MRIs).Methodology: In this study, we review the different approaches that have been used in computer-aided detection and segmentation of MS lesions. Our review resulted in categorizing MS lesion segmentation approaches into six broad categories: data-driven, statistical, supervised machine learning, unsupervised machine learning, fuzzy, and deep learning-based techniques. We critically analyze the different techniques under these approaches and highlight their strengths and weaknesses.

show abstract

“…Post processing stage attempts to connect pixels with the same label and highlights abnormal regions. In addition, as many of the segmentation methods potentially come up with many false positives and negatives, post-processing phase attempts to increase both sensitivity and specificity of the segmentation by reducing these false positives and negatives, respectively [5]. Evaluation as final stage of the pipeline measures performance of segmentation method quantitatively by utilizing relevant performance measures.…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of Abnormal Tissues in Medical Images

Khastavaneh

Ebrahimpour-Komleh

2019

J Biomed Phys Eng

View full text Add to dashboard Cite

Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentation is a preliminary step for these measurements and also further analysis. Manual segmentation of abnormalities is cumbersome, error prone, and subjective. As a result, automated segmentation of abnormal tissue is a need. In this study, representative techniques for segmentation of abnormal tissues are reviewed. Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions. As experimental results demonstrate, the methods based on deep learning techniques perform better than other methods that are usually based on handy feature engineering techniques. Finally, the most common measures to evaluate automated abnormal tissue segmentation methods are reported.

show abstract

False Positives Reduction on Segmented Multiple Sclerosis Lesions Using Fuzzy Inference System by Incorporating Atlas Prior Anatomical Knowledge: A Conceptual Model

Cited by 3 publications

References 12 publications

BIANCA‐MS: An optimized tool for automated multiple sclerosis lesion segmentation

BIANCA‐MS: An optimized tool for automated multiple sclerosis lesion segmentation

A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs

Automated Segmentation of Abnormal Tissues in Medical Images

Contact Info

Product

Resources

About