Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure

Ratiometric spectral imaging for fast tumor detection and chemotherapy monitoring in vivo

We report a novel in vivo spectral imaging approach to cancer detection and chemotherapy assessment. We describe and characterize a ratiometric spectral imaging and analysis method and evaluate its performance for tumor detection and delineation by quantitatively monitoring the specific accumulation of targeted gallium corrole (HerGa) into HER2-positive (HER2 +) breast tumors. HerGa temporal accumulation in nude mice bearing HER2 + breast tumors was monitored comparatively by a. this new ratiometric imaging and analysis method; b. established (reflectance and fluorescence) spectral imaging; c. more commonly used fluorescence intensity imaging. We also tested the feasibility of HerGa imaging in vivo using the ratiometric spectral imaging method for tumor detection and delineation. Our results show that the new method not only provides better quantitative information than typical spectral imaging, but also better specificity than standard fluorescence intensity imaging, thus allowing enhanced in vivo outlining of tumors and dynamic, quantitative monitoring of targeted chemotherapy agent accumulation into them

Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures, but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues.Comment: 10 pages, 9 figure

Non-invasive measurement of hemodynamic change during 8 MHz transcranial focused ultrasound stimulation using near-infrared spectroscopy

Background: Transcranial focused ultrasound (tFUS) attracts wide attention in neuroscience as an effective noninvasive approach to modulate brain circuits. In spite of this, the effects of tFUS on the brain is still unclear, and further investigation is needed. The present study proposes to use near-infrared spectroscopy (NIRS) to observe cerebral hemodynamic change caused by tFUS in a noninvasive manner. Results: The results show a transient increase of oxyhemoglobin and decrease of deoxyhemoglobin concentration in the mouse model induced by ultrasound stimulation of the somatosensory cortex with a frequency of 8 MHz but not in sham. In addition, the amplitude of hemodynamics change can be related to the peak intensity of the acoustic wave. Conclusion: High frequency 8 MHz ultrasound was shown to induce hemodynamic changes measured using NIRS through the intact mouse head. The implementation of NIRS offers the possibility of investigating brain response noninvasively for different tFUS parameters through cerebral hemodynamic change. © 2019 The Author(s).1

Fighting Cancer with Corroles

Corroles are exceptionally promising platforms for the development of agents for simultaneous cancer-targeting imaging and therapy. Depending on the element chelated by the corrole, these theranostic agents may be tuned primarily for diagnostic or therapeutic function. Versatile synthetic methodologies allow for the preparation of amphipolar derivatives, which form stable noncovalent conjugates with targeting biomolecules. These conjugates can be engineered for imaging and targeting as well as therapeutic function within one theranostic assembly. In this review, we begin with a brief outline of corrole chemistry that has been uniquely useful in designing corrole-based anticancer agents. Then we turn attention to the early literature regarding corrole anticancer activity, which commenced one year after the first scalable synthesis was reported (1999–2000). In 2001, a major advance was made with the introduction of negatively charged corroles, as these molecules, being amphipolar, form stable conjugates with many proteins. More recently, both cellular uptake and intracellular trafficking of metallocorroles have been documented in experimental investigations employing advanced optical spectroscopic as well as magnetic resonance imaging techniques. Key results from work on both cellular and animal models are reviewed, with emphasis on those that have shed new light on the mechanisms associated with anticancer activity. In closing, we predict a very bright future for corrole anticancer research, as it is experiencing exponential growth, taking full advantage of recently developed imaging and therapeutic modalities

Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap

A single-beam acoustic trapping technique has been shown to be very useful for determining the invasiveness of suspended breast cancer cells in an acoustic trap with a manual calcium analysis method. However, for the rapid translation of the technology into the clinic, the development of an efficient/accurate analytical method is needed. We, therefore, develop a fully-automatic deep learning-based calcium image analysis algorithm for determining the invasiveness of suspended breast cancer cells using a single-beam acoustic trapping system. The algorithm allows to segment cells, find trapped cells, and quantify their calcium changes over time. For better segmentation of calcium fluorescent cells even with vague boundaries, a novel deep learning architecture with multi-scale/multi-channel convolution operations (MM-Net) is devised and constructed by a target inversion training method. The MM-Net outperforms other deep learning models in the cell segmentation. Also, a detection/quantification algorithm is developed and implemented to automatically determine the invasiveness of a trapped cell. For the evaluation of the algorithm, it is applied to quantify the invasiveness of breast cancer cells. The results show that the algorithm offers similar performance to the manual calcium analysis method for determining the invasiveness of cancer cells, suggesting that it may serve as a novel tool to automatically determine the invasiveness of cancer cells with high-efficiency. © 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.1

Smartphone-based multispectral imaging: system development and potential for mobile skin diagnosis

We investigate the potential of mobile smartphone-based multispectral imaging for the quantitative diagnosis and management of skin lesions. Recently, various mobile devices such as a smartphone have emerged as healthcare tools. They have been applied for the early diagnosis of nonmalignant and malignant skin diseases. Particularly, when they are combined with an advanced optical imaging technique such as multispectral imaging and analysis, it would be beneficial for the early diagnosis of such skin diseases and for further quantitative prognosis monitoring after treatment at home. Thus, we demonstrate here the development of a smartphone-based multispectral imaging system with high portability and its potential for mobile skin diagnosis. The results suggest that smartphone-based multispectral imaging and analysis has great potential as a healthcare tool for quantitative mobile skin diagnosis. © 2016 Optical Society of America.1

Jitter reduction technique for acoustic radiation force impulse microscopy via photoacoustic detection

We demonstrate a jitter noise reduction technique for acoustic radiation force impulse microscopy via photoacoustic detection (PA-ARFI), which promises to be capable of measuring cell mechanics. To reduce the jitter noise induced by Q-switched pulsed laser operated at high repetition frequency, photoacoustic signals from the surface of an ultrasound transducer are aligned by cross-correlation and peak-to-peak detection, respectively. Each method is then employed to measure the displacements of a target sample in an agar phantom and a breast cancer cell due to ARFI application, followed by the quantitative comparison between their performances. The suggested methods for PA-ARFI significantly reduce jitter noises, thus allowing us to measure displacements of a target cell due to ARFI application by less than 3 μm. © 2015 Optical Society of America.1

Investigating photoexcitation-induced mitochondrial damage by chemotherapeutic corroles using multimode optical imaging

We recently reported that a targeted, brightly fluorescent gallium corrole (HerGa) is highly effective for breast tumor detection and treatment. Unlike structurally similar porphryins, HerGa exhibits tumor-targeted toxicity without the need for photoexcitation. We have now examined whether photoexcitation further modulates HerGa toxicity, using multimode optical imaging of live cells, including two-photon excited fluorescence, differential interference contrast (DIC), spectral, and lifetime imaging. Using two-photon excited fluorescence imaging, we observed that light at specific wavelengths augments the HerGa-mediated mitochondrial membrane potential disruption of breast cancer cells in situ. In addition, DIC, spectral, and fluorescence lifetime imaging enabled us to both validate cell damage by HerGa photoexcitation and investigate HerGa internalization, thus allowing optimization of light dose and timing. Our demonstration of HerGa phototoxicity opens the way for development of new methods of cancer intervention using tumor-targeted corroles