A two-stage Map-matching Method for Sparse Trajectories with Small-scale Ground Truth Data (S)
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CTによる膀胱腫瘍浸潤度判定をより客観的なものとする目的で, 新たに理想膀胱外壁線および腫瘍根部径 (W)-縦径 (H) 比を設定して各浸潤度別に比較検討し, 以下の検討結果をえた. (1) CTによる形態診断で, 小乳頭状型, 乳頭状有茎性型, 乳頭状無茎性型を示したものはすべて Stage B1以下であつた. 広基結節型を示した23例中19例は, CTによる判定通り広基結節型 Stage B2以上であつたが, 残りの4例は摘出標本では乳頭状無茎性型 Stage B1であつた. (2) 体位変換時の腫瘍変位性は, 乳頭状有茎性型形態を示したもののみに認められ, そのすべてが Stage B1以下であつた. (3) 理想膀胱外壁線外への腫瘍突出は広基結節型を示した Stage C 以上の全例に認められた. また, 従来膀胱壁外には突出しないとされてきた Stage B2の6例中4例に認められた. 突出した腫瘍根部の外壁を比較すると Stage B2ではその突出部の性状は平滑であつたが, Stage C 以上の場合は不整であつた. (4) W/H比に関しては, 乳頭状型を呈した Stage B1以下と広基結節型を呈した Stage B2以上は1.2で明瞭に判別できた.以上より作成したCTによる膀胱腫瘍浸潤度判定基準により, Stage B1以下では37例中26例 (70%), Stage B2以上では19例中16例 (84%). 特に膀胱内注入物質として空気を用いた場合, Stage B2以上において15例中15例 (100%) と非常に高い一致率が得られた.本判定基準による膀胱腫瘍浸潤度のCT診断は, 従来は極めて困難であつた深層浸潤性腫瘍の各 Stage の判別診断を可能とし, 臨床上極めて有用なものと考える.
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We compare methodologies for trainable document image content extraction, using a variety of ground-truth policies: loose, tight, and pixel-accurate. The goal is to achieve pixel-accurate segmentation of document images. Which ground-truth policy is the best has been debated. ``Loose'' truth is obtained by sweeping rectangles to enclose entire text blocks etc, and can be an efficient manual task. ``Tight'' truth requires more care, and more time, to enclose individual text lines. Pixel-accurate truth, in which only foreground pixels are labeled, can be obtained by applying the PARC PixLabeler tool; in our experience this tool was as quick to use as loose truthing. We have compared the accuracy of all three truthing policies, and report that tight truth supports higher accuracy than loose truth, and pixel-accurate truth yields the highest accuracy. We have also experimented on morphological expansions on pixel-accurate truth, by expanding sets of foreground pixels morphologically, and report that expanded pixel-accurate truth supports higher accuracy than pixel-accurate truth.
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보행 분석은 사람 식별, 소아마비, 척수손상, 뇌졸중 환자 등의 운동 평가 및 재활, 퇴행성 뇌질환 진단 등 다양한 분야에서 활용되고 있다. 보행 분석에 있어서 모션 캡쳐의 Ground-Truth 데이터는 광학식 방식을 이용한 Vicon 또는 Optotrak 시스템 등으로부터 산출된 데이터이나, 이들 시스템은 마커를 부착해야하는 점과, 설치 공간상의 제약 및 상당한 고가의 시스템이라는 단점을 갖고 있다. 최근 보행 분석에 있어서 이러한 단점을 보완하고자 Microsoft 사의 Kinect 센서 여러개로 구성된 시스템으로부터 3D 공간에서 body point 위치 데이터를 추출하는 연구가 진행 중이다. 그러나 기존 연구는 제안 시스템의 유효성 평가에 있어서 Ground-Truth 데이터와 제안 시스템 산출 데이터 간의 선형상관관계, 재현가능성, 일치성 등을 증명할 뿐, Kinect 데이터를 기반으로 Ground-Truth 데이터를 모델링하고 그 값을 예측하는 연구는 아직 미비한 상황이다. 본 연구에서는 다변수 다중회귀분석을 이용하여 다중 Kinect 시스템으로부터 산출된 데이터를 기반으로 Ground-Truth 데이터를 모델링하고 예측하는 방법을 고찰하였고, 모델값과 실제 Ground-Truth 데이터값 간의 결정계수와 급내상관계수를 계산함으로써 상관관계에 대하여 분석하였다.
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In the field of multi-view people localization, only a few works consider a non-planar ground surface. In this article we introduce a framework for collecting ground truth data in such case, we show characterization of specific errors and introduce a method to automatically merge multiple ground truth data generated by different users to form a more reliable reference ground truth. We use this reference ground truth to evaluate the error rate, the accuracy and the recall of subjects (6 laymen and 3 with domain knowledge). We show that even laymen can work accurately, but even subjects with domain knowledge miss a number of people in a crowded scene. Our findings show that creating ground truth data requires special attention in this field.
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In this paper we describe a methodology for measuring the ground truth for boundary detection methods in medical images. An interface is used to allow medical experts to outline boundaries of interest on images. Following this, the accuracy of the experts in repeating the ground truth detection process is evaluated. Finally, an approach is described for comparing the ground truth as provided by experts with actual boundaries detected automatically. Experimental results are presented to validate the proposed algorithms.
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地方性甲状腺腫は臨床, 病理学的に複雑な経過を示し, 疫学的, 病理学的発生論や治療の選択に多くの難題が残されている. 著者は本症の病期検討および妥当な病期分類がこれらの検索, 解明にきわめて有用であると着目し, 甲状腺剔出を行なった地方性甲状腺腫336例を臨床, 病理学的に精査し下記の結果をえた. 1) 地方性甲状腺腫は臨床的, 病理学的経過からStage 1;過形成期, Stage 2;腫大期, Stage 3;結節形成期と分類できた. 2) 本症は病期の進行に伴い病悩期間は長くなり, 甲状腺腫は増大し種々の局所圧迫症状をみるが, 合併症がなければ全身的, 臨床生化学的所見はほぼ正常である. 3) 臨床, 病理学的に本症はStage 1からStage 2さらにStage 3に進行し, Stage 3は終末期である. 4) 病変の占居部位はStage 1では両葉性, Stage 2では両葉性と単葉性がほぼ等しく, Stage 3では単葉性が多い点からもStageの進行度を裏付けられる. 5) 336例のうち男性39例, 女性297例, 男女比1:7.6で, 発生のピークは女性では20才から30才代, 男性は30才から40才代であった. 6) 手術適応例は若年者より成人に多く, 女性は男性より著しく多い. ヨード治療の効果が若年者ほど良好で, 男性は女性よりもヨード感受性が高いためである. 7) Stage 3の9.4%に甲状腺機能亢進症 (4.03%), 腺腫 (1.34%), 甲状腺癌 (4.03%) などの共存疾患がみられた. 8) ヨード治療はStage 1では効果的であるがStage 2では無効でStage 3に進行し, 種々の合併症を起こすこともあり, Stage 2における手術が望ましい.
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腎細胞癌50例 (stage 1が12例, stage 2が15例, stage 3Aが5例, stage 3Bが2例, stage 3Cが1例, stage 4Aが1例, stage 4Bが14例) の腎動脈撮影時における静脈像所見について検討を行つた.腎動脈撮影で腎静脈本幹が描出されたものは, 50例中21例 (42%) で, 正常腎の腎静脈描出率83% (30例中25例) より低かつた. また患側別では, 右側が28例中9例 (32%), 左側が22例中12例 (55%) であり, 右腎静脈は左腎静脈と比較して描出率が低かつた. 一方, high stage (stage 3およびstage 4)は, low stage (stage 1およびstage 2) に比べて, 腎静脈描出率が低かつたが, high stageのうち腎静脈腫瘍血栓を除けば, 腎静脈描出率は low stage のそれと同程度であつた.腎動静脈瘻は50例中6例 (12%) に認めたが, そのうち腎静脈腫瘍血栓は3例 (50%) と高率に合併していたが, その予後は必ずしも悪くない傾向であつた.腎静脈腫瘍血栓を認めた13例中, 腎動脈撮影で striated vascular pattern は10例 (77%) に描出された.側副静脈は50例中11例 (22%) に認めたが, そのうち腎静脈腫瘍血栓の合併は5例 (45%) で, 残り6例は腎静脈腫瘍血栓が認められないにもかかわらず, 側副静脈が描出されており, 腎静脈腫瘍血栓と側副静脈描出の関連性は少ないと考えられた.
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The accuracy of a binarization algorithm is often calculated relative to a ground truth image. Except for synthetically generated images, no ground truth image exists. Evaluating binarization on real images is preferred. The ground truthing between and among different operators is compared. Four direct metrics were used. The variability of the results of five different automatic binarization algorithms were compared to that of manual ground truth results. Significant variability in the ground truth results was found.
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