Purpose: To investigate the difference of stereometric parameters of optic nerve head between the normal subjects and patients with big-cupped disk and primary open angle glaucoma (POAG).Methods: Twenty-two cases (44 ...Purpose: To investigate the difference of stereometric parameters of optic nerve head between the normal subjects and patients with big-cupped disk and primary open angle glaucoma (POAG).Methods: Twenty-two cases (44 eyes) of normal subjects, 17 cases (34 eyes) of patients with big-cupped disk and 19 cases (37 eyes) of patients with POAG underwent Heidelberg Retina Tomograph (HRT) examination to get topography images and stereometric parameters of optic nerve head.Results: The stereometric parameters of optic nerve head of the normal, patients with big-cupped disk and POAG were 1) disk area (mm2): 1. 995± 0. 501, 2. 407±0. 661 and 2. 248±0.498; 2) cup area (mm2): 0.573±0.264, 1. 095±0. 673 and 1. 340±0. 516; 3) cup/disk ratio: 0. 25±0. 095, 0. 428±0. 176 and 0. 589±0.195; 4) rim area (mm2): 1.461±0.328, 1.312±0.418 and 0. 905± 0.409; 5)cup volume (mm3): 0. 108±0. 073, 0. 347±0. 346 and 0. 550 ±0. 394; 6) rim volume (mm3): 0. 421±0. 111, 0. 378±0. 225 and 0. 224±0. 189; 7) mean cup展开更多
视杯、视盘的区域信息对青光眼的诊断具有重要意义。为提高视网膜图像中杯盘分割的准确性,提出DMSwin-Unet模型。它以Swin-Unet为主干,融合MMAS(Multi-scale Mixed Aggregation and Selection)机制提高瓶颈层的感受野,增强边界与细节特...视杯、视盘的区域信息对青光眼的诊断具有重要意义。为提高视网膜图像中杯盘分割的准确性,提出DMSwin-Unet模型。它以Swin-Unet为主干,融合MMAS(Multi-scale Mixed Aggregation and Selection)机制提高瓶颈层的感受野,增强边界与细节特征的捕获能力;同时通过DCA(Dual Cross-Attention)模块加强跳跃连接中的语义信息交互,提升上下文建模能力。此外,结合杯盘边界模糊、区域不平滑的特点设计了混合损失函数,进一步优化分割边界。在REFUGE、ORIGA、Drishti-GS数据集上,DMSwin-Unet分别取得了视杯Dice分数:89.06%、91.28%、93.35%;视盘Dice分数:96.46%、98.06%、97.85%。实验结果表明,该模型在视杯与视盘分割任务中均优于现有方法,具备良好的临床应用潜力。展开更多
Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood v...Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed challenging.Spatially Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary correspondingly.This research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting model.The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here.There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels.The ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative SBEFCM.In terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.展开更多
目的探讨生理性大视杯随时间推移,有无形态学变化。方法对200只生理性大视杯眼进行随访,每间隔3个月随访1次,每眼至少随访12个月以上。随访项目包括,各视乳头参数、眼压、视野、眼轴长度以及屈光度等。结果符合上述随访要求的有148只生...目的探讨生理性大视杯随时间推移,有无形态学变化。方法对200只生理性大视杯眼进行随访,每间隔3个月随访1次,每眼至少随访12个月以上。随访项目包括,各视乳头参数、眼压、视野、眼轴长度以及屈光度等。结果符合上述随访要求的有148只生理性大视杯眼,平均随访16个月。发现视杯面积(P<0.05),杯盘面积比、视杯容积、盘沿容积、平均视杯深度、最大视杯深度、轮廓线高度变化、平均视网膜神经纤维层厚度、视网膜神经纤维层横截面积均变大(P<0.01);盘沿面积变小(P<0.05);视盘面积、杯形测量无显著性变化。眼压值变小(P<0.01),视野 MS 变大、MD 变小(P<0.01),眼轴变长(P<0.01),近视加深(P<0.01)。结论经随访,生理性大视杯形态结构参数有一定变化,但无青光眼性神经损害。(中国眼耳鼻喉科杂志,2006,6:164～166)展开更多
文摘Purpose: To investigate the difference of stereometric parameters of optic nerve head between the normal subjects and patients with big-cupped disk and primary open angle glaucoma (POAG).Methods: Twenty-two cases (44 eyes) of normal subjects, 17 cases (34 eyes) of patients with big-cupped disk and 19 cases (37 eyes) of patients with POAG underwent Heidelberg Retina Tomograph (HRT) examination to get topography images and stereometric parameters of optic nerve head.Results: The stereometric parameters of optic nerve head of the normal, patients with big-cupped disk and POAG were 1) disk area (mm2): 1. 995± 0. 501, 2. 407±0. 661 and 2. 248±0.498; 2) cup area (mm2): 0.573±0.264, 1. 095±0. 673 and 1. 340±0. 516; 3) cup/disk ratio: 0. 25±0. 095, 0. 428±0. 176 and 0. 589±0.195; 4) rim area (mm2): 1.461±0.328, 1.312±0.418 and 0. 905± 0.409; 5)cup volume (mm3): 0. 108±0. 073, 0. 347±0. 346 and 0. 550 ±0. 394; 6) rim volume (mm3): 0. 421±0. 111, 0. 378±0. 225 and 0. 224±0. 189; 7) mean cup
文摘Irretrievable loss of vision is the predominant result of Glaucoma in the retina.Recently,multiple approaches have paid attention to the automatic detection of glaucoma on fundus images.Due to the interlace of blood vessels and the herculean task involved in glaucoma detection,the exactly affected site of the optic disc of whether small or big size cup,is deemed challenging.Spatially Based Ellipse Fitting Curve Model(SBEFCM)classification is suggested based on the Ensemble for a reliable diagnosis of Glaucomain theOptic Cup(OC)and Optic Disc(OD)boundary correspondingly.This research deploys the Ensemble Convolutional Neural Network(CNN)classification for classifying Glaucoma or Diabetes Retinopathy(DR).The detection of the boundary between the OC and the OD is performed by the SBEFCM,which is the latest weighted ellipse fitting model.The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here.There is a preprocessing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels.The ascertaining of OCandODboundary,which characterizedmany output factors for glaucoma detection,has been developed by EnsembleCNNclassification,which includes detecting sensitivity,specificity,precision,andArea Under the receiver operating characteristic Curve(AUC)values accurately by an innovative SBEFCM.In terms of contrast,the proposed Ensemble CNNsignificantly outperformed the current methods.
文摘目的探讨生理性大视杯随时间推移,有无形态学变化。方法对200只生理性大视杯眼进行随访,每间隔3个月随访1次,每眼至少随访12个月以上。随访项目包括,各视乳头参数、眼压、视野、眼轴长度以及屈光度等。结果符合上述随访要求的有148只生理性大视杯眼,平均随访16个月。发现视杯面积(P<0.05),杯盘面积比、视杯容积、盘沿容积、平均视杯深度、最大视杯深度、轮廓线高度变化、平均视网膜神经纤维层厚度、视网膜神经纤维层横截面积均变大(P<0.01);盘沿面积变小(P<0.05);视盘面积、杯形测量无显著性变化。眼压值变小(P<0.01),视野 MS 变大、MD 变小(P<0.01),眼轴变长(P<0.01),近视加深(P<0.01)。结论经随访,生理性大视杯形态结构参数有一定变化,但无青光眼性神经损害。(中国眼耳鼻喉科杂志,2006,6:164～166)
