Automated Extraction of the Cortical Sulci Based on a Supervised Learning Approach

Tu, Zhuowen; Zheng, Songfeng; Yuille, Alan; Reiss, Allan L.; Dutton, Rebecca A.; Lee, A.D.; Galaburda, Albert M.; Dinov, Ivo D.; Thompson, Paul M.; Toga, Arthur W.

doi:10.1109/tmi.2007.892506

Cited by 57 publications

(44 citation statements)

References 25 publications

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“…Boosting was proposed in [11,12] and has since found applications in many areas including biomedical imaging [13]. A popular boosting algorithm is AdaBoost [14].…”

Section: Supervised Classification On the Coordinatesmentioning

confidence: 99%

Cortical Surface Thickness as a Classifier: Boosting for Autism Classification

Singh

Mukherjee

Chung

2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. We study the problem of classifying an autistic group from controls using structural image data alone, a task that requires a clinical interview with a psychologist. Because of the highly convoluted brain surface topology, feature extraction poses the first obstacle. A clinically relevant measure called the cortical thickness has shown promise but yields a rather challenging learning problem -where the dimensionality of the distribution is extremely large and the training set is small. By observing that each point on the brain cortical surface may be treated as a "hypothesis", we propose a new algorithm for LPBoosting (with truncated neighborhoods) for this problem. In addition to learning a high quality classifier, our model incorporates topological priors into the classification framework directly -that two neighboring points on the cortical surface (hypothesis pairs) must have similar discriminative qualities. As a result, we obtain not just a label {+1, −1} for test items, but also an indication of the "discriminative regions" on the cortical surface. We discuss the formulation and present interesting experimental results.

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“…Boosting was proposed in [11,12] and has since found applications in many areas including biomedical imaging [13]. A popular boosting algorithm is AdaBoost [14].…”

Section: Supervised Classification On the Coordinatesmentioning

confidence: 99%

Cortical Surface Thickness as a Classifier: Boosting for Autism Classification

Singh

Mukherjee

Chung

2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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“…In contrast, our approach easily scales to large training datasets and high-dimensional feature spaces, often found in medical imaging [15], [16], [17]. Moreover, unlike other methods, our approach does not require tuning any parameter except those needed by the boosted classifier it relies on.…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptation for Microscopy Imaging

Becker

Christoudias

Fua

2015

IEEE Trans. Med. Imaging

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Abstract-Electron and Light Microscopy imaging can now deliver high-quality image stacks of neural structures. However, the amount of human annotation effort required to analyze them remains a major bottleneck. While Machine Learning algorithms can be used to help automate this process, they require training data, which is time-consuming to obtain manually, especially in image stacks. Furthermore, due to changing experimental conditions, successive stacks often exhibit differences that are severe enough to make it difficult to use a classifier trained for a specific one on another. This means that this tedious annotation process has to be repeated for each new stack. In this paper we present a domain adaptation algorithm that addresses this issue by effectively leveraging labeled examples across different acquisitions and significantly reducing the annotation requirements. Our approach can handle complex, non-linear image feature transformations and scales to large microscopy datasets that often involve high-dimensional feature spaces and large 3D data volumes. We evaluate our approach on four challenging Electron and Light Microscopy applications that exhibit very different image modalities and where annotation is very costly. Across all applications we achieve a significant improvement over the state-of-the-art Machine Learning methods and demonstrate our ability to greatly reduce human annotation effort.

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“…Unfortunately, their use in cortical analysis [14] has been limited due to the high geometrical variability of the folding pattern across individuals. Classifying cortical data on larger training sets may capture additional shape variability, however, one may wonder how to better exploit existing data, and how to capture maximal information on such complex surfaces.…”

Section: Introductionmentioning

confidence: 99%